p_table <- function(tab_data, ...) {
tab_data_2 <- deparse(substitute(tab_data))
table_p <- do.call(CreateTableOne,
list(data = as.name(tab_data_2), includeNA = TRUE, ...))
table_p_out <- print(table_p,
showAllLevels = TRUE,
printToggle = FALSE)
kable(table_p_out,
align = "c")
}
uni_var <- function(test_var, data_imp) {
cat("_________________________________________________")
cat("\n")
cat(" \n##", test_var)
cat("\n")
cat("_________________________________________________")
cat("\n")
f <- as.formula(paste("Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)",
as.name(test_var),
sep = " ~ " ))
data_imp_2 <- deparse(substitute(data_imp))
km_fit <- do.call("survfit", list(formula = f, data = as.name(data_imp_2)))
print(km_fit)
cat("\n")
print(summary(km_fit, times = c(12, 24, 36, 48, 60, 120)))
cat("\n")
cat("\n")
cat("\n")
cat(" \n## Univariable Cox Proportional Hazard Model for: ", test_var)
cat("\n")
cat("\n")
n_levels <- nlevels(data_imp[[test_var]])
if(n_levels == 1){
print("Only one level, no Cox model performed")
cat("\n")
} else {
cox_fit <- do.call("coxph", list(formula = f, data = as.name(data_imp_2)))
print(summary(cox_fit))
cat("\n")
do.call("ggforest",
list(model = cox_fit, data = as.name(data_imp_2)))
}
cat("\n")
cat("\n")
cat("\n")
cat(" \n## Unadjusted Kaplan Meier Overall Survival Curve for: ", test_var)
p <- do.call("ggsurvplot",
list(fit = km_fit, data = as.name(data_imp_2),
palette = "jco", censor = FALSE, legend = "right",
linetype = "strata", xlab = "Time (Months)"))
print(p)
}
col.width <- c(37, 10, 1, 1, 3, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 8, 2, 2, 2, 4, 4, 1, 4, 1, 1,
1, 3, 2, 2, 8, 2, 5, 5, 5, 4, 5, 5, 5,4, 2, 1, 2, 1, 3, 1, 1, 1, 1, 1, 1, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 8,
8, 8, 2, 1, 1, 1, 1, 8, 1, 1, 8, 1, 1, 2, 2, 5, 2, 5, 3, 1, 3, 1, 8, 8, 2, 8,
2, 8, 2, 2, 1, 8, 1, 1, 1, 1, 1, 8, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 3, 1, 1,
1, 1, 1, 1, 1, 1, 1)
col.names.abr <- c("PUF_CASE_ID", "PUF_FACILITY_ID", "FACILITY_TYPE_CD", "FACILITY_LOCATION_CD",
"AGE", "SEX", "RACE", "SPANISH_HISPANIC_ORIGIN", "INSURANCE_STATUS",
"MED_INC_QUAR_00", "NO_HSD_QUAR_00", "UR_CD_03", "MED_INC_QUAR_12", "NO_HSD_QUAR_12",
"UR_CD_13", "CROWFLY", "CDCC_TOTAL_BEST", "SEQUENCE_NUMBER", "CLASS_OF_CASE",
"YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "LATERALITY", "HISTOLOGY", "BEHAVIOR", "GRADE",
"DIAGNOSTIC_CONFIRMATION", "TUMOR_SIZE", "REGIONAL_NODES_POSITIVE",
"REGIONAL_NODES_EXAMINED", "DX_STAGING_PROC_DAYS", "RX_SUMM_DXSTG_PROC", "TNM_CLIN_T",
"TNM_CLIN_N", "TNM_CLIN_M", "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
"TNM_PATH_STAGE_GROUP", "TNM_EDITION_NUMBER", "ANALYTIC_STAGE_GROUP", "CS_METS_AT_DX",
"CS_METS_EVAL", "CS_EXTENSION", "CS_TUMOR_SIZEEXT_EVAL", "CS_METS_DX_BONE", "CS_METS_DX_BRAIN",
"CS_METS_DX_LIVER", "CS_METS_DX_LUNG", "LYMPH_VASCULAR_INVASION", "CS_SITESPECIFIC_FACTOR_1",
"CS_SITESPECIFIC_FACTOR_2", "CS_SITESPECIFIC_FACTOR_3", "CS_SITESPECIFIC_FACTOR_4",
"CS_SITESPECIFIC_FACTOR_5", "CS_SITESPECIFIC_FACTOR_6", "CS_SITESPECIFIC_FACTOR_7",
"CS_SITESPECIFIC_FACTOR_8", "CS_SITESPECIFIC_FACTOR_9", "CS_SITESPECIFIC_FACTOR_10",
"CS_SITESPECIFIC_FACTOR_11", "CS_SITESPECIFIC_FACTOR_12", "CS_SITESPECIFIC_FACTOR_13",
"CS_SITESPECIFIC_FACTOR_14", "CS_SITESPECIFIC_FACTOR_15", "CS_SITESPECIFIC_FACTOR_16",
"CS_SITESPECIFIC_FACTOR_17", "CS_SITESPECIFIC_FACTOR_18", "CS_SITESPECIFIC_FACTOR_19",
"CS_SITESPECIFIC_FACTOR_20", "CS_SITESPECIFIC_FACTOR_21", "CS_SITESPECIFIC_FACTOR_22",
"CS_SITESPECIFIC_FACTOR_23", "CS_SITESPECIFIC_FACTOR_24", "CS_SITESPECIFIC_FACTOR_25",
"CS_VERSION_LATEST", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS", "DX_DEFSURG_STARTED_DAYS",
"RX_SUMM_SURG_PRIM_SITE", "RX_HOSP_SURG_APPR_2010", "RX_SUMM_SURGICAL_MARGINS",
"RX_SUMM_SCOPE_REG_LN_SUR", "RX_SUMM_SURG_OTH_REGDIS", "SURG_DISCHARGE_DAYS", "READM_HOSP_30_DAYS",
"REASON_FOR_NO_SURGERY", "DX_RAD_STARTED_DAYS", "RX_SUMM_RADIATION", "RAD_LOCATION_OF_RX",
"RAD_TREAT_VOL", "RAD_REGIONAL_RX_MODALITY", "RAD_REGIONAL_DOSE_CGY", "RAD_BOOST_RX_MODALITY",
"RAD_BOOST_DOSE_CGY", "RAD_NUM_TREAT_VOL", "RX_SUMM_SURGRAD_SEQ", "RAD_ELAPSED_RX_DAYS",
"REASON_FOR_NO_RADIATION", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", "RX_SUMM_CHEMO",
"DX_HORMONE_STARTED_DAYS", "RX_SUMM_HORMONE", "DX_IMMUNO_STARTED_DAYS", "RX_SUMM_IMMUNOTHERAPY",
"RX_SUMM_TRNSPLNT_ENDO", "RX_SUMM_SYSTEMIC_SUR_SEQ", "DX_OTHER_STARTED_DAYS", "RX_SUMM_OTHER",
"PALLIATIVE_CARE", "RX_SUMM_TREATMENT_STATUS", "PUF_30_DAY_MORT_CD", "PUF_90_DAY_MORT_CD",
"DX_LASTCONTACT_DEATH_MONTHS", "PUF_VITAL_STATUS", "RX_HOSP_SURG_PRIM_SITE", "RX_HOSP_CHEMO",
"RX_HOSP_IMMUNOTHERAPY", "RX_HOSP_HORMONE", "RX_HOSP_OTHER", "PUF_MULT_SOURCE", "REFERENCE_DATE_FLAG",
"RX_SUMM_SCOPE_REG_LN_2012", "RX_HOSP_DXSTG_PROC", "PALLIATIVE_CARE_HOSP", "TUMOR_SIZE_SUMMARY",
"METS_AT_DX_OTHER", "METS_AT_DX_DISTANT_LN", "METS_AT_DX_BONE", "METS_AT_DX_BRAIN",
"METS_AT_DX_LIVER", "METS_AT_DX_LUNG", "NO_HSD_QUAR_16", "MED_INC_QUAR_16", "MEDICAID_EXPN_CODE")
#Read in data for each subsite
lip <- read_fwf('NCDBPUF_Lip.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
melanoma <- read_fwf('NCDBPUF_Melanoma.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
skin <- read_fwf('NCDBPUF_OtSkin.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
hodgextr <- read_fwf('NCDBPUF_HodgExtr.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
hodgndal <- read_fwf('NCDBPUF_HodgNdal.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
NHLndal <- read_fwf('NCDBPUF_NHLNdal.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
NHLextr <- read_fwf('NCDBPUF_NHLExtr.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
breast <- read_fwf('NCDBPUF_Breast.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
vulva <- read_fwf('NCDBPUF_Vulva.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
vagina <- read_fwf('NCDBPUF_Vagina.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
penis <- read_fwf('NCDBPUF_Penis.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
otleuk <- read_fwf('NCDBPUF_OtLeuk.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
otheracuteleuk <- read_fwf('NCDBPUF_OtAcLeuk.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
ALL <- read_fwf('NCDBPUF_ALymLeuk.3.2016.0.dat',
fwf_widths(col.width, col_names = col.names.abr),
col_types = cols(.default = col_character()))
#Combine data for all subsites
dat <- bind_rows(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast,
vulva, vagina, penis, NHLextr, otleuk, otheracuteleuk, ALL)
rm(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast, vulva, vagina,
penis, NHLextr, otleuk, otheracuteleuk, ALL)
prim_site_text <- data_frame(PRIMARY_SITE = c(
#NHL sites
"C000",
"C001",
"C002",
"C003",
"C004",
"C005",
"C006",
"C008",
"C009",
"C019",
"C020",
"C021",
"C022",
"C023",
"C024",
"C028",
"C029",
"C030",
"C031",
"C039",
"C040",
"C041",
"C048",
"C049",
"C050",
"C051",
"C052",
"C058",
"C059",
"C060",
"C061",
"C062",
"C068",
"C069",
"C079",
"C098",
"C099",
"C111",
"C142",
"C300",
"C379",
"C420",
"C422",
"C424",
#skin/melanoma
"C440", "C441", "C442", "C443", "C444", "C445",
"C446", "C447", "C448", "C449",
#breast - nipple
"C500",
#vagina/vulva
"C510", "C511", "C512", "C518", "C519", "C529",
#penis
"C600", "C601", "C602", "C608", "C609", "C639",
"C770",
"C771",
"C772",
"C773",
"C774",
"C775",
"C778",
"C779"),
SITE_TEXT = c(
"C00.0 External Lip: Upper NOS",
"C00.1 External Lip: Lower NOS",
"C00.2 External Lip: NOS",
"C00.3 Lip: Upper Mucosa",
"C00.4 Lip: Lower Mucosa",
"C00.5 Lip: Mucosa NOS",
"C00.6 Lip: Commissure",
"C00.8 Lip: Overlapping",
"C00.9 Lip NOS",
"C01.9 Tongue: Base NOS",
"C02.0 Tongue: Dorsal NOS",
"C02.1 Tongue: Border, Tip",
"C02.2 Tongue: Ventral NOS",
"C02.3 Tongue: Anterior NOS",
"C02.4 Lingual Tonsil",
"C02.8 Tongue: Overlapping",
"C02.9 Tongue: NOS",
"C03.0 Gum: Upper",
"C03.1 Gum: Lower",
"C03.9 Gum NOS",
"C04.0 Mouth: Anterior Floor",
"C04.1 Mouth: Lateral Floor",
"C04.8 Mouth: Overlapping Floor",
"C04.9 Floor of Mouth NOS",
"C05.0 Hard Palate",
"C05.1 Soft Palate NOS",
"C05.2 Uvula",
"C05.8 Palate: Overlapping",
"C05.9 Palate NOS",
"C06.0 Cheek Mucosa",
"C06.1 Mouth: Vestibule",
"C06.2 Retromolar Area",
"C06.8 Mouth: Other Overlapping",
"C06.9 Mouth NOS",
"C07.9 Parotid Gland",
"C09.8 Tonsil: Overlapping",
"C09.9 Tonsil NOS",
"C11.1 Nasopharynx: Poster Wall",
"C14.2 Waldeyer Ring",
"C30.0 Nasal Cavity",
"C37.9 Thymus",
"C42.0 Blood",
"C42.2 Spleen",
"C42.4 Hematopoietic NOS",
#skin
"C44.0 Skin of lip, NOS",
"C44.1 Eyelid",
"C44.2 External ear",
"C44.3 Skin of ear and unspecified parts of face",
"C44.4 Skin of scalp and neck",
"C44.5 Skin of trunk",
"C44.6 Skin of upper limb and shoulder",
"C44.7 Skin of lower limb and hip",
"C44.8 Overlapping lesion of skin",
"C44.9 Skin, NOS",
#breast
"C50.0 Nipple",
#vulva/vagina
"C51.0 Labium majus",
"C51.1 Labium minus",
"C51.2 Clitoris",
"C51.8 Overlapping lesion of vulva",
"C51.9 Vulva, NOS",
"C52.9 Vagina, NOS",
#penis
"C60.0 Prepuce",
"C60.1 Glans penis",
"C60.2 Body of penis",
"C60.8 Overlapping lesion of penis",
"C60.9 Penis",
"C63.2 Scrotum, NOS",
"C77.0 Lymph Nodes: HeadFaceNeck",
"C77.1 Intrathoracic Lymph Nodes",
"C77.2 Intra-abdominal LymphNodes",
"C77.3 Lymph Nodes of axilla or arm ",
"C77.4 Lymph Nodes: Leg",
"C77.5 Pelvic Lymph Nodes",
"C77.8 Lymph Nodes: multiple region",
"C77.9 Lymph Node NOS"))
dat <- merge(dat, prim_site_text, by = "PRIMARY_SITE", all.x = TRUE)
rm(prim_site_text)
# convert numeric variables from character class to numeric class
num_vars <- c("AGE", "CROWFLY", "TUMOR_SIZE", "DX_STAGING_PROC_DAYS", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
"DX_DEFSURG_STARTED_DAYS", "SURG_DISCHARGE_DAYS", "DX_RAD_STARTED_DAYS", "RAD_REGIONAL_DOSE_CGY",
"RAD_BOOST_DOSE_CGY", "RAD_ELAPSED_RX_DAYS", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS",
"DX_HORMONE_STARTED_DAYS", "DX_OTHER_STARTED_DAYS", "DX_LASTCONTACT_DEATH_MONTHS",
"RAD_NUM_TREAT_VOL")
dat[num_vars] <- lapply(dat[num_vars], as.numeric)
# convert factor variables from character class to factor class
vars <- names(dat)
fact_vars <- vars[!(vars %in% num_vars)] # basically all of the non-numerics
dat[fact_vars] <- lapply(dat[fact_vars], as.character)
dat[fact_vars] <- lapply(dat[fact_vars], as.factor)
dat <- dat %>%
mutate(FACILITY_TYPE_F = fct_recode(FACILITY_TYPE_CD,
"Community Cancer Program" = "1",
"Comprehensive Comm Ca Program" = "2",
"Academic/Research Program" = "3",
"Integrated Network Ca Program" = "4",
"Other" = "9")) %>%
mutate(FACILITY_LOCATION_F = fct_recode(FACILITY_LOCATION_CD,
"New England" = "1",
"Middle Atlantic" = "2",
"South Atlantic" = "3",
"East North Central" = "4",
"East South Central" = "5",
"West North Central" = "6",
"West South Central" = "7",
"Mountain" = "8",
"Pacific" = "9",
"out of US" = "0")) %>%
mutate(FACILITY_GEOGRAPHY = fct_collapse(FACILITY_LOCATION_CD,
"Northeast" = c("1", "2"),
"South" = c("3", "7"),
"Midwest" = c("4", "5", "6"),
"West" = c("8", "9"))) %>%
mutate(AGE_F = cut(AGE, c(0, 54, 64, 74, 100))) %>%
mutate(AGE_40 = cut(AGE, c(0, 40, 100))) %>%
mutate(SEX_F = fct_recode(SEX,
"Male" = "1",
"Female" = "2")) %>%
mutate(RACE_F = fct_collapse(RACE,
"White" = c("01"),
"Black" = c("02"),
"Asian" = c("04", "05", "06", "07", "08", "10", "11", "12", "13", "14", "15",
"16", "17", "20", "21", "22", "25", "26", "27", "28", "30", "31",
"32", "96", "97"),
"Other/Unk" = c("03", "98", "99"))) %>%
mutate(HISPANIC = fct_collapse(SPANISH_HISPANIC_ORIGIN,
"Yes" = c("1", "2", "3", "4", "5", "6", "7", "8"),
"No" = c("0"),
"Unknown" = c("9"))) %>%
mutate(INSURANCE_F = fct_recode(INSURANCE_STATUS,
"None" = "0",
"Private" = "1",
"Medicaid" = "2",
"Medicare" = "3",
"Other Government" = "4",
"Unknown" = "9")) %>%
mutate(INSURANCE_F = fct_relevel(INSURANCE_F,
"Private")) %>%
mutate(INCOME_F = fct_recode(MED_INC_QUAR_12,
"Less than $38,000" = "1",
"$38,000 - $47,999" = "2",
"$48,000 - $62,999" = "3",
"$63,000 +" = "4")) %>%
mutate(EDUCATION_F = fct_recode(NO_HSD_QUAR_12,
"21% or more" = "1",
"13 - 20.9%" = "2",
"7 - 12.9%" = "3",
"Less than 7%" = "4")) %>%
mutate(U_R_F = fct_collapse(UR_CD_13,
"Metro" = c("1", "2", "3"),
"Urban" = c("4", "5", "6", "7"),
"Rural" = c("8", "9"))) %>%
mutate(CLASS_OF_CASE_F = fct_collapse(CLASS_OF_CASE,
All_Part_Prim = c("10", "11", "12", "13",
"14", "20", "21", "22"),
Other_Facility = c("00"))) %>%
mutate(GRADE_F = fct_recode(GRADE,
"Gr I: Well Diff" = "1",
"Gr II: Mod Diff" = "2",
"Gr III: Poor Diff" = "3",
"Gr IV: Undiff/Anaplastic" = "4",
"NA/Unkown" = "9")) %>%
mutate(HISTOLOGY_F = fct_infreq(HISTOLOGY)) %>%
mutate(HISTOLOGY_F = factor(HISTOLOGY_F)) %>%
mutate(HISTOLOGY_F_LIM = fct_lump(HISTOLOGY_F, prop = 0.05)) %>%
mutate(TNM_CLIN_T = fct_recode(TNM_CLIN_T,
"N_A" = "88")) %>%
mutate(TNM_CLIN_T = fct_relevel(TNM_CLIN_T,
"1")) %>%
mutate(TNM_CLIN_N = fct_recode(TNM_CLIN_N,
"N_A" = "88")) %>%
mutate(TNM_CLIN_M = fct_recode(TNM_CLIN_M,
"N_A" = "88")) %>%
mutate(TNM_PATH_T = fct_recode(TNM_PATH_T,
"N_A" = "88")) %>%
mutate(TNM_PATH_T = fct_relevel(TNM_PATH_T,
"1")) %>%
mutate(TNM_PATH_N = fct_recode(TNM_PATH_N,
"N_A" = "88")) %>%
mutate(TNM_PATH_M = fct_recode(TNM_PATH_M,
"N_A" = "88")) %>%
mutate(TNM_CLIN_STAGE_GROUP = fct_recode(TNM_CLIN_STAGE_GROUP,
"N_A" = "88")) %>%
mutate(TNM_PATH_STAGE_GROUP = fct_recode(TNM_PATH_STAGE_GROUP,
"N_A" = "88")) %>%
mutate(MARGINS = fct_recode(RX_SUMM_SURGICAL_MARGINS,
"No Residual" = "0",
"Residual, NOS" = "1",
"Microscopic Resid" = "2",
"Macroscopic Resid" = "3",
"Not evaluable" = "7",
"No surg" = "8",
"Unknown" = "9")) %>%
mutate(MARGINS_YN = fct_collapse(RX_SUMM_SURGICAL_MARGINS,
"Yes" = c("1", "2", "3"),
"No" = c("0"),
"No surg/Unk/NA" = c("7", "8", "9"))) %>%
mutate(READM_HOSP_30_DAYS_F = fct_recode(READM_HOSP_30_DAYS,
"No_Surg_or_No_Readmit" = "0",
"Unplan_Readmit_Same" = "1",
"Plan_Readmit_Same" = "2",
"PlanUnplan_Same" = "3",
"Unknown" = "4")) %>%
mutate(RX_SUMM_RADIATION_F = fct_recode(RX_SUMM_RADIATION,
"None" = "0",
"Beam Radiation" = "1",
"Radioactive Implants" = "2",
"Radioisotopes" = "3",
"Beam + Imp or Isotopes" = "4",
"Radiation, NOS" = "5",
"Unknown" = "9")) %>%
mutate(PUF_30_DAY_MORT_CD_F = fct_recode(PUF_30_DAY_MORT_CD,
"Alive_30" = "0",
"Dead_30" = "1",
"Unknown" = "9")) %>%
mutate(PUF_90_DAY_MORT_CD_F = fct_recode(PUF_90_DAY_MORT_CD,
"Alive_90" = "0",
"Dead_90" = "1",
"Unknown" = "9")) %>%
mutate(LYMPH_VASCULAR_INVASION_F = fct_recode(LYMPH_VASCULAR_INVASION,
"Neg_LymphVasc_Inv" = "0",
"Pos_LumphVasc_Inv" = "1",
"N_A" = "8",
"Unknown" = "9")) %>%
mutate(RX_HOSP_SURG_APPR_2010_F = fct_recode(RX_HOSP_SURG_APPR_2010,
"No_Surg" = "0",
"Robot_Assist" = "1",
"Robot_to_Open" = "2",
"Endo_Lap" = "3",
"Endo_Lap_to_Open" = "4",
"Open_Unknown" = "5",
"Unknown" = "9")) %>%
mutate(All = "All") %>%
mutate(All = factor(All)) %>%
mutate(REASON_FOR_NO_SURGERY_F = fct_recode(REASON_FOR_NO_SURGERY,
"Surg performed" = "0",
"Surg not recommended" = "1",
"No surg due to pt factors" = "2",
"No surg, pt died" = "5",
"Surg rec, not done" = "6",
"Surg rec, pt refused" = "7",
"Surg rec, unk if done" = "8",
"Unknown" = "9")) %>%
mutate(SURGERY_YN = ifelse(REASON_FOR_NO_SURGERY == "0",
"Yes",
ifelse(REASON_FOR_NO_SURGERY == "9",
"Ukn",
"No"))) %>%
mutate(SURG_TF = case_when(SURGERY_YN == "Yes" ~ TRUE,
SURGERY_YN == "No" ~ FALSE,
TRUE ~ NA)) %>%
mutate(REASON_FOR_NO_RADIATION_F = fct_recode(REASON_FOR_NO_RADIATION,
"Rad performed" = "0",
"Rad not recommended" = "1",
"No Rad due to pt factors" = "2",
"No Rad, pt died" = "5",
"Rad rec, not done" = "6",
"Rad rec, pt refused" = "7",
"Rad rec, unk if done" = "8",
"Unknown" = "9")) %>%
mutate(RADIATION_YN = ifelse(REASON_FOR_NO_RADIATION == "0",
"Yes",
ifelse(REASON_FOR_NO_RADIATION == "9",
NA,
"No"))) %>%
mutate(SURGRAD_SEQ_F = fct_recode(RX_SUMM_SURGRAD_SEQ,
"None or Surg or Rad" = "0",
"Rad before Surg" = "2",
"Surg before Rad" = "3",
"Rad before and after Surg" = "4",
"Intraop Rad" = "5",
"Intraop Rad plus other" = "6",
"Unknown" = "9")) %>%
mutate(SURG_RAD_SEQ = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
"Surg Alone",
ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
"Rad Alone",
ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0",
"No Treatment",
ifelse(RX_SUMM_SURGRAD_SEQ == "2",
"Rad then Surg",
ifelse(RX_SUMM_SURGRAD_SEQ == "3",
"Surg then Rad",
ifelse(RX_SUMM_SURGRAD_SEQ == "4",
"Rad before and after Surg",
"Other"))))))) %>%
mutate(SURG_RAD_SEQ = fct_relevel(SURG_RAD_SEQ,
"Surg Alone",
"Surg then Rad",
"Rad Alone")) %>%
mutate(CHEMO_YN = fct_collapse(RX_SUMM_CHEMO,
"No" = c("00", "82", "85", "86", "87"),
"Yes" = c("01", "02", "03"),
"Ukn" = c("88", "99"))) %>%
mutate(IMMUNO_YN = fct_collapse(RX_SUMM_IMMUNOTHERAPY,
"No" = c("00", "82", "85", "86", "87"),
"Yes" = c("01"),
"Ukn" = c("88", "99"))) %>%
mutate(SURG_RAD_SEQ_C = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
"Surg, No rad, No Chemo",
ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
"Rad, No Surg, No Chemo",
ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
"No Surg, No Rad, No Chemo",
ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "No",
"Rad then Surg, No Chemo",
ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "No",
"Surg then Rad, No Chemo",
ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "No",
"Rad before and after Surg, No Chemo",
ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
"Surg, No rad, Yes Chemo",
ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
"Rad, No Surg, Yes Chemo",
ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
"No Surg, No Rad, Yes Chemo",
ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "Yes",
"Rad then Surg, Yes Chemo",
ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "Yes",
"Surg then Rad, Yes Chemo",
ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "Yes",
"Rad before and after Surg, Yes Chemo",
"Other"))))))))))))) %>%
mutate(SURG_RAD_SEQ_C = fct_infreq(SURG_RAD_SEQ_C)) %>%
mutate(T_SIZE = as.numeric(TUMOR_SIZE)) %>%
mutate(T_SIZE = ifelse(T_SIZE == 0,
"No Tumor",
ifelse(T_SIZE > 0 & T_SIZE < 10 | T_SIZE == 991,
"< 1 cm",
ifelse(T_SIZE >= 10 & T_SIZE < 20 | T_SIZE == 992,
"1-2 cm",
ifelse(T_SIZE >= 20 & T_SIZE < 30 | T_SIZE == 993,
"2-3 cm",
ifelse(T_SIZE >= 30 & T_SIZE < 40 | T_SIZE == 994,
"3-4 cm",
ifelse(T_SIZE >= 40 & T_SIZE < 50 | T_SIZE == 995,
"4-5 cm",
ifelse(T_SIZE >= 50 & T_SIZE < 60 | T_SIZE == 996,
"5-6 cm",
ifelse(T_SIZE >= 60 & T_SIZE <= 987 |
T_SIZE == 980 | T_SIZE == 989 |
T_SIZE == 997,
">6 cm",
ifelse(T_SIZE == 988 | T_SIZE == 999,
"NA_unk",
"Microscopic focus")))))))))) %>%
mutate(T_SIZE = factor(T_SIZE)) %>%
mutate(T_SIZE = fct_relevel(T_SIZE,
"No Tumor", "Microscopic focus", "< 1 cm", "1-2 cm", "2-3 cm", "3-4 cm",
"4-5 cm", "5-6 cm", ">6 cm", "NA_unk")) %>%
mutate(mets_at_dx = case_when(CS_METS_DX_LUNG == "1" ~ "Lung",
CS_METS_DX_BONE == "1" ~ "Bone",
CS_METS_DX_BRAIN == "1" ~ "Brain",
CS_METS_DX_LIVER == "1" ~ "Liver",
TRUE ~ "None/Other/Unk/NA")) %>%
mutate(MEDICAID_EXPN_CODE = fct_recode(MEDICAID_EXPN_CODE,
"Non-Expansion State" = "0",
"Jan 2014 Expansion States" = "1",
"Early Expansion States (2010-13)" = "2",
"Late Expansion States (> Jan 2014)" = "3",
"Suppressed for Ages 0 - 39" = "9")) %>%
mutate(EXPN_GROUP = case_when(MEDICAID_EXPN_CODE %in% c("Jan 2014 Expansion States") &
YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Post-Expansion",
MEDICAID_EXPN_CODE %in% c("Jan 2014 Expansion States") &
YEAR_OF_DIAGNOSIS %in%
c("2004", "2005", "2006", "2007", "2008",
"2009", "2010", "2011", "2012", "2013") ~ "Pre-Expansion",
MEDICAID_EXPN_CODE %in% c("Early Expansion States (2010-13)") &
YEAR_OF_DIAGNOSIS %in% c("2010", "2011", "2012", "2013", "2014", "2015") ~ "Post-Expansion",
MEDICAID_EXPN_CODE %in% c("Early Expansion States (2010-13)") &
YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", "2009") ~ "Pre-Expansion",
MEDICAID_EXPN_CODE %in% c("Non-Expansion State") ~ "Pre-Expansion",
MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") ~ "Pre-Expansion",
MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") &
YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Exclude",
MEDICAID_EXPN_CODE == "Suppressed for Ages 0 - 39" ~ "Exclude")) %>%
mutate(pre_2014 = YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008",
"2009", "2010", "2011", "2012", "2013")) %>%
mutate(mets_at_dx_F = ifelse(mets_at_dx == "None/Other/Unk/NA", FALSE, TRUE)) %>%
mutate(Tx_YN = ifelse(SURG_RAD_SEQ == "No Treatment" & CHEMO_YN == "No" &
IMMUNO_YN == "No", FALSE,
ifelse(CHEMO_YN == "Ukn", NA,
TRUE)))
fact_vars_2 <- c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "AGE_F", "SEX_F", "RACE_F",
"HISPANIC", "INSURANCE_F", "INCOME_F", "EDUCATION_F", "U_R_F",
"CDCC_TOTAL_BEST", "CLASS_OF_CASE_F", "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "HISTOLOGY",
"BEHAVIOR", "GRADE_F", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
"TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M", "TNM_PATH_STAGE_GROUP",
"MARGINS", "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
"PUF_90_DAY_MORT_CD_F", "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "mets_at_dx")
dat <- dat %>%
mutate_at(fact_vars_2, funs(factor(.)))
Table of variables for all cases:
data <- data %>% droplevels()
p_table(data,
vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY", "AGE", "AGE_F", "AGE_40",
"SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F",
"INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
"SITE_TEXT", "HISTOLOGY", "BEHAVIOR", "GRADE_F",
"DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
"TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
"TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
"DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
"READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
"PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS",
"LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
"SURG_RAD_SEQ_C", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN", "IMMUNO_YN", "Tx_YN", "mets_at_dx",
"MEDICAID_EXPN_CODE", "EXPN_GROUP", "YEAR_OF_DIAGNOSIS"))
| n |
|
48 |
| FACILITY_TYPE_F (%) |
Community Cancer Program |
1 ( 2.1) |
|
Comprehensive Comm Ca Program |
13 ( 27.1) |
|
Academic/Research Program |
20 ( 41.7) |
|
Integrated Network Ca Program |
3 ( 6.2) |
|
NA |
11 ( 22.9) |
| FACILITY_LOCATION_F (%) |
New England |
5 ( 10.4) |
|
Middle Atlantic |
4 ( 8.3) |
|
South Atlantic |
9 ( 18.8) |
|
East North Central |
7 ( 14.6) |
|
East South Central |
1 ( 2.1) |
|
West North Central |
2 ( 4.2) |
|
West South Central |
1 ( 2.1) |
|
Mountain |
1 ( 2.1) |
|
Pacific |
7 ( 14.6) |
|
NA |
11 ( 22.9) |
| FACILITY_GEOGRAPHY (%) |
Northeast |
9 ( 18.8) |
|
South |
10 ( 20.8) |
|
Midwest |
10 ( 20.8) |
|
West |
8 ( 16.7) |
|
NA |
11 ( 22.9) |
| AGE (mean (sd)) |
|
54.12 (17.85) |
| AGE_F (%) |
(0,54] |
22 ( 45.8) |
|
(54,64] |
12 ( 25.0) |
|
(64,74] |
5 ( 10.4) |
|
(74,100] |
9 ( 18.8) |
| AGE_40 (%) |
(0,40] |
12 ( 25.0) |
|
(40,100] |
36 ( 75.0) |
| SEX_F (%) |
Male |
30 ( 62.5) |
|
Female |
18 ( 37.5) |
| RACE_F (%) |
White |
38 ( 79.2) |
|
Black |
7 ( 14.6) |
|
Other/Unk |
2 ( 4.2) |
|
Asian |
1 ( 2.1) |
| HISPANIC (%) |
No |
43 ( 89.6) |
|
Yes |
3 ( 6.2) |
|
Unknown |
2 ( 4.2) |
| INSURANCE_F (%) |
Private |
29 ( 60.4) |
|
None |
2 ( 4.2) |
|
Medicaid |
3 ( 6.2) |
|
Medicare |
12 ( 25.0) |
|
Other Government |
1 ( 2.1) |
|
Unknown |
1 ( 2.1) |
| INCOME_F (%) |
Less than $38,000 |
10 ( 20.8) |
|
$38,000 - $47,999 |
10 ( 20.8) |
|
$48,000 - $62,999 |
14 ( 29.2) |
|
$63,000 + |
14 ( 29.2) |
| EDUCATION_F (%) |
21% or more |
6 ( 12.5) |
|
13 - 20.9% |
14 ( 29.2) |
|
7 - 12.9% |
15 ( 31.2) |
|
Less than 7% |
13 ( 27.1) |
| U_R_F (%) |
Metro |
39 ( 81.2) |
|
Urban |
8 ( 16.7) |
|
Rural |
1 ( 2.1) |
| CROWFLY (mean (sd)) |
|
40.32 (72.27) |
| CDCC_TOTAL_BEST (%) |
0 |
40 ( 83.3) |
|
1 |
8 ( 16.7) |
| SITE_TEXT (%) |
C44.3 Skin of ear and unspecified parts of face |
2 ( 4.2) |
|
C44.5 Skin of trunk |
4 ( 8.3) |
|
C44.6 Skin of upper limb and shoulder |
2 ( 4.2) |
|
C44.7 Skin of lower limb and hip |
11 ( 22.9) |
|
C44.8 Overlapping lesion of skin |
3 ( 6.2) |
|
C44.9 Skin, NOS |
26 ( 54.2) |
| HISTOLOGY (%) |
9726 |
48 (100.0) |
| BEHAVIOR (%) |
3 |
48 (100.0) |
| GRADE_F (%) |
5 |
48 (100.0) |
| DX_STAGING_PROC_DAYS (mean (sd)) |
|
3.92 (15.33) |
| TNM_CLIN_T (%) |
N_A |
44 ( 91.7) |
|
NA |
4 ( 8.3) |
| TNM_CLIN_N (%) |
N_A |
44 ( 91.7) |
|
NA |
4 ( 8.3) |
| TNM_CLIN_M (%) |
N_A |
44 ( 91.7) |
|
NA |
4 ( 8.3) |
| TNM_CLIN_STAGE_GROUP (%) |
1 |
11 ( 22.9) |
|
1A |
8 ( 16.7) |
|
1B |
4 ( 8.3) |
|
2B |
3 ( 6.2) |
|
3 |
2 ( 4.2) |
|
3B |
1 ( 2.1) |
|
4 |
3 ( 6.2) |
|
4A |
8 ( 16.7) |
|
4B |
4 ( 8.3) |
|
99 |
4 ( 8.3) |
| TNM_PATH_T (%) |
N_A |
41 ( 85.4) |
|
NA |
7 ( 14.6) |
| TNM_PATH_N (%) |
N_A |
41 ( 85.4) |
|
NA |
7 ( 14.6) |
| TNM_PATH_M (%) |
N_A |
38 ( 79.2) |
|
NA |
10 ( 20.8) |
| TNM_PATH_STAGE_GROUP (%) |
1 |
3 ( 6.2) |
|
1B |
2 ( 4.2) |
|
3 |
2 ( 4.2) |
|
3B |
1 ( 2.1) |
|
4A |
4 ( 8.3) |
|
99 |
21 ( 43.8) |
|
NA |
15 ( 31.2) |
| DX_RX_STARTED_DAYS (mean (sd)) |
|
36.46 (38.49) |
| DX_SURG_STARTED_DAYS (mean (sd)) |
|
7.17 (17.55) |
| DX_DEFSURG_STARTED_DAYS (mean (sd)) |
|
7.17 (17.55) |
| MARGINS (%) |
No Residual |
1 ( 2.1) |
|
Not evaluable |
1 ( 2.1) |
|
No surg |
42 ( 87.5) |
|
Unknown |
4 ( 8.3) |
| MARGINS_YN (%) |
No |
1 ( 2.1) |
|
No surg/Unk/NA |
47 ( 97.9) |
| SURG_DISCHARGE_DAYS (mean (sd)) |
|
25.50 (46.45) |
| READM_HOSP_30_DAYS_F (%) |
No_Surg_or_No_Readmit |
47 ( 97.9) |
|
9 |
1 ( 2.1) |
| RX_SUMM_RADIATION_F (%) |
None |
39 ( 81.2) |
|
Beam Radiation |
9 ( 18.8) |
| PUF_30_DAY_MORT_CD_F (%) |
Alive_30 |
5 ( 10.4) |
|
Unknown |
1 ( 2.1) |
|
NA |
42 ( 87.5) |
| PUF_90_DAY_MORT_CD_F (%) |
Alive_90 |
5 ( 10.4) |
|
Unknown |
1 ( 2.1) |
|
NA |
42 ( 87.5) |
| DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) |
|
22.68 (24.83) |
| LYMPH_VASCULAR_INVASION_F (%) |
N_A |
48 (100.0) |
| RX_HOSP_SURG_APPR_2010_F (%) |
No_Surg |
42 ( 87.5) |
|
Open_Unknown |
6 ( 12.5) |
| SURG_RAD_SEQ (%) |
Surg Alone |
4 ( 8.3) |
|
Surg then Rad |
2 ( 4.2) |
|
Rad Alone |
7 ( 14.6) |
|
No Treatment |
35 ( 72.9) |
| SURG_RAD_SEQ_C (%) |
Surg, No rad, No Chemo |
2 ( 4.2) |
|
Surg then Rad, Yes Chemo |
2 ( 4.2) |
|
Surg, No rad, Yes Chemo |
2 ( 4.2) |
|
No Surg, No Rad, Yes Chemo |
21 ( 43.8) |
|
No Surg, No Rad, No Chemo |
14 ( 29.2) |
|
Rad, No Surg, Yes Chemo |
4 ( 8.3) |
|
Rad, No Surg, No Chemo |
3 ( 6.2) |
| SURGERY_YN (%) |
No |
42 ( 87.5) |
|
Yes |
6 ( 12.5) |
| RADIATION_YN (%) |
No |
39 ( 81.2) |
|
Yes |
9 ( 18.8) |
| CHEMO_YN (%) |
No |
19 ( 39.6) |
|
Yes |
29 ( 60.4) |
| IMMUNO_YN (%) |
No |
44 ( 91.7) |
|
Yes |
4 ( 8.3) |
| Tx_YN (%) |
FALSE |
12 ( 25.0) |
|
TRUE |
36 ( 75.0) |
| mets_at_dx (%) |
None/Other/Unk/NA |
48 (100.0) |
| MEDICAID_EXPN_CODE (%) |
Non-Expansion State |
13 ( 27.1) |
|
Jan 2014 Expansion States |
13 ( 27.1) |
|
Early Expansion States (2010-13) |
8 ( 16.7) |
|
Late Expansion States (> Jan 2014) |
3 ( 6.2) |
|
Suppressed for Ages 0 - 39 |
11 ( 22.9) |
| EXPN_GROUP (%) |
Exclude |
11 ( 22.9) |
|
Post-Expansion |
12 ( 25.0) |
|
Pre-Expansion |
25 ( 52.1) |
| YEAR_OF_DIAGNOSIS (%) |
2010 |
4 ( 8.3) |
|
2011 |
3 ( 6.2) |
|
2012 |
9 ( 18.8) |
|
2013 |
10 ( 20.8) |
|
2014 |
12 ( 25.0) |
|
2015 |
10 ( 20.8) |
preExpMedicare <- nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion" & INSURANCE_F == "Medicare"))
postExpMedicare <- nrow(data %>% filter(EXPN_GROUP == "Post-Expansion" & INSURANCE_F == "Medicare"))
# p = 0.25 when comparing change in proportion of patients with Medicare before and after ACA expansion
prop.test(c(preExpMedicare, postExpMedicare),
c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% filter(EXPN_GROUP == "Post-Expansion"))))
Chi-squared approximation may be incorrect
2-sample test for equality of proportions with continuity correction
data: c(preExpMedicare, postExpMedicare) out of c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% c(preExpMedicare, postExpMedicare) out of filter(EXPN_GROUP == "Post-Expansion")))
X-squared = 0.041225, df = 1, p-value = 0.8391
alternative hypothesis: two.sided
95 percent confidence interval:
-0.4699054 0.2832387
sample estimates:
prop 1 prop 2
0.2400000 0.3333333
preExpNoInsurance <- nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion") %>%
filter(INSURANCE_F == "None"))
postExpNoInsurance <- nrow(data %>% filter(EXPN_GROUP == "Post-Expansion") %>%
filter(INSURANCE_F == "None"))
# Significant decrease in the overall proportion of patients without insurance after ACA expansion
prop.test(c(preExpNoInsurance, postExpNoInsurance),
c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% filter(EXPN_GROUP == "Post-Expansion"))))
Chi-squared approximation may be incorrect
2-sample test for equality of proportions with continuity correction
data: c(preExpNoInsurance, postExpNoInsurance) out of c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% c(preExpNoInsurance, postExpNoInsurance) out of filter(EXPN_GROUP == "Post-Expansion")))
X-squared = 2.5713e-32, df = 1, p-value = 1
alternative hypothesis: two.sided
95 percent confidence interval:
-0.2608913 0.1742246
sample estimates:
prop 1 prop 2
0.04000000 0.08333333
p_table(no_Excludes, strata = "EXPN_GROUP", vars = "DX_RX_STARTED_DAYS")
| n |
|
248 |
1137 |
|
|
| DX_RX_STARTED_DAYS (mean (sd)) |
|
48.96 (44.78) |
48.91 (143.60) |
0.996 |
|
data <- data %>% mutate(Insured = INSURANCE_F != "Unknown")
Kaplan Meier Analysis
All
uni_var(test_var = "All", data_imp = data)
_________________________________________________
## All
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ All, data = data)
n events median 0.95LCL 0.95UCL
48.00 26.00 18.73 8.08 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ All, data = data)
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 27 18 0.611 0.0718 0.485 0.769
24 16 8 0.419 0.0749 0.295 0.595
36 12 0 0.419 0.0749 0.295 0.595
48 9 0 0.419 0.0749 0.295 0.595
60 4 0 0.419 0.0749 0.295 0.595
## Univariable Cox Proportional Hazard Model for: All
[1] "Only one level, no Cox model performed"
## Unadjusted Kaplan Meier Overall Survival Curve for: All
Facility Type
uni_var(test_var = "FACILITY_TYPE_F", data_imp = data)
_________________________________________________
## FACILITY_TYPE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_TYPE_F, data = data)
11 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
FACILITY_TYPE_F=Community Cancer Program 1 0 NA NA NA
FACILITY_TYPE_F=Comprehensive Comm Ca Program 13 6 23.9 12.09 NA
FACILITY_TYPE_F=Academic/Research Program 20 13 12.2 4.76 NA
FACILITY_TYPE_F=Integrated Network Ca Program 3 2 14.2 2.20 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_TYPE_F, data = data)
11 observations deleted due to missingness
FACILITY_TYPE_F=Community Cancer Program
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 0 1 0 1 1
24 1 0 1 0 1 1
FACILITY_TYPE_F=Comprehensive Comm Ca Program
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 8 3 0.750 0.125 0.541 1.000
24 4 3 0.438 0.157 0.216 0.884
36 4 0 0.438 0.157 0.216 0.884
48 3 0 0.438 0.157 0.216 0.884
60 1 0 0.438 0.157 0.216 0.884
FACILITY_TYPE_F=Academic/Research Program
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 10 9 0.533 0.114 0.351 0.811
24 4 4 0.305 0.109 0.151 0.614
36 3 0 0.305 0.109 0.151 0.614
48 2 0 0.305 0.109 0.151 0.614
60 2 0 0.305 0.109 0.151 0.614
FACILITY_TYPE_F=Integrated Network Ca Program
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 1 0.667 0.272 0.2995 1
24 1 1 0.333 0.272 0.0673 1
36 1 0 0.333 0.272 0.0673 1
48 1 0 0.333 0.272 0.0673 1
## Univariable Cox Proportional Hazard Model for: FACILITY_TYPE_F
Loglik converged before variable 1,2,3 ; beta may be infinite.
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_TYPE_F, data = data)
n= 37, number of events= 21
(11 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
FACILITY_TYPE_FComprehensive Comm Ca Program 1.780e+01 5.394e+07 8.479e+03 0.002 0.998
FACILITY_TYPE_FAcademic/Research Program 1.829e+01 8.750e+07 8.479e+03 0.002 0.998
FACILITY_TYPE_FIntegrated Network Ca Program 1.817e+01 7.788e+07 8.479e+03 0.002 0.998
exp(coef) exp(-coef) lower .95 upper .95
FACILITY_TYPE_FComprehensive Comm Ca Program 53936494 1.854e-08 0 Inf
FACILITY_TYPE_FAcademic/Research Program 87500977 1.143e-08 0 Inf
FACILITY_TYPE_FIntegrated Network Ca Program 77881204 1.284e-08 0 Inf
Concordance= 0.586 (se = 0.063 )
Rsquare= 0.078 (max possible= 0.972 )
Likelihood ratio test= 3 on 3 df, p=0.3923
Wald test = 0.96 on 3 df, p=0.8114
Score (logrank) test = 2.04 on 3 df, p=0.5641
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 1 rows containing missing values (geom_errorbar).Removed 4 rows containing missing values (geom_text).Removed 4 rows containing missing values (geom_text).Removed 4 rows containing missing values (geom_text).Removed 4 rows containing missing values (geom_text).Removed 4 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).
## Unadjusted Kaplan Meier Overall Survival Curve for: FACILITY_TYPE_F
Facility Location
uni_var(test_var = "FACILITY_LOCATION_F", data_imp = data)
_________________________________________________
## FACILITY_LOCATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_LOCATION_F, data = data)
11 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
FACILITY_LOCATION_F=New England 5 4 8.08 3.15 NA
FACILITY_LOCATION_F=Middle Atlantic 4 3 20.69 7.49 NA
FACILITY_LOCATION_F=South Atlantic 9 4 23.95 12.09 NA
FACILITY_LOCATION_F=East North Central 7 5 4.76 2.20 NA
FACILITY_LOCATION_F=East South Central 1 0 NA NA NA
FACILITY_LOCATION_F=West North Central 2 1 16.36 16.36 NA
FACILITY_LOCATION_F=West South Central 1 0 NA NA NA
FACILITY_LOCATION_F=Mountain 1 1 14.16 NA NA
FACILITY_LOCATION_F=Pacific 7 3 6.14 2.04 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_LOCATION_F, data = data)
11 observations deleted due to missingness
FACILITY_LOCATION_F=New England
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 3 0.4 0.219 0.1367 1
24 1 1 0.2 0.179 0.0346 1
36 1 0 0.2 0.179 0.0346 1
48 1 0 0.2 0.179 0.0346 1
60 1 0 0.2 0.179 0.0346 1
FACILITY_LOCATION_F=Middle Atlantic
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 3 1 0.75 0.217 0.4259 1
24 1 2 0.25 0.217 0.0458 1
36 1 0 0.25 0.217 0.0458 1
FACILITY_LOCATION_F=South Atlantic
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 6 2 0.778 0.139 0.549 1
24 3 2 0.486 0.187 0.228 1
36 3 0 0.486 0.187 0.228 1
48 3 0 0.486 0.187 0.228 1
FACILITY_LOCATION_F=East North Central
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 3 4 0.429 0.187 0.182 1
24 1 1 0.214 0.178 0.042 1
36 1 0 0.214 0.178 0.042 1
48 1 0 0.214 0.178 0.042 1
60 1 0 0.214 0.178 0.042 1
FACILITY_LOCATION_F=East South Central
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 0 1 0 1 1
24 1 0 1 0 1 1
36 1 0 1 0 1 1
FACILITY_LOCATION_F=West North Central
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 0 1.0 0.000 1.000 1
24 1 1 0.5 0.354 0.125 1
FACILITY_LOCATION_F=West South Central
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 0 1 0 1 1
24 1 0 1 0 1 1
FACILITY_LOCATION_F=Mountain
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 0 1 0 1 1
FACILITY_LOCATION_F=Pacific
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 3 0.5 0.204 0.225 1
24 1 0 0.5 0.204 0.225 1
36 1 0 0.5 0.204 0.225 1
48 1 0 0.5 0.204 0.225 1
60 1 0 0.5 0.204 0.225 1
## Univariable Cox Proportional Hazard Model for: FACILITY_LOCATION_F
Loglik converged before variable 4,6 ; beta may be infinite.
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_LOCATION_F, data = data)
n= 37, number of events= 21
(11 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
FACILITY_LOCATION_FMiddle Atlantic -5.607e-01 5.708e-01 7.685e-01 -0.730 0.466
FACILITY_LOCATION_FSouth Atlantic -8.513e-01 4.269e-01 7.118e-01 -1.196 0.232
FACILITY_LOCATION_FEast North Central -1.411e-01 8.684e-01 6.743e-01 -0.209 0.834
FACILITY_LOCATION_FEast South Central -1.874e+01 7.301e-09 8.826e+03 -0.002 0.998
FACILITY_LOCATION_FWest North Central -1.065e+00 3.448e-01 1.122e+00 -0.949 0.343
FACILITY_LOCATION_FWest South Central -1.874e+01 7.301e-09 8.826e+03 -0.002 0.998
FACILITY_LOCATION_FMountain -2.833e-02 9.721e-01 1.126e+00 -0.025 0.980
FACILITY_LOCATION_FPacific -5.527e-01 5.754e-01 7.657e-01 -0.722 0.470
exp(coef) exp(-coef) lower .95 upper .95
FACILITY_LOCATION_FMiddle Atlantic 5.708e-01 1.752e+00 0.12657 2.574
FACILITY_LOCATION_FSouth Atlantic 4.269e-01 2.343e+00 0.10578 1.722
FACILITY_LOCATION_FEast North Central 8.684e-01 1.151e+00 0.23161 3.256
FACILITY_LOCATION_FEast South Central 7.301e-09 1.370e+08 0.00000 Inf
FACILITY_LOCATION_FWest North Central 3.448e-01 2.900e+00 0.03823 3.111
FACILITY_LOCATION_FWest South Central 7.301e-09 1.370e+08 0.00000 Inf
FACILITY_LOCATION_FMountain 9.721e-01 1.029e+00 0.10701 8.830
FACILITY_LOCATION_FPacific 5.754e-01 1.738e+00 0.12829 2.581
Concordance= 0.641 (se = 0.068 )
Rsquare= 0.163 (max possible= 0.972 )
Likelihood ratio test= 6.59 on 8 df, p=0.5812
Wald test = 2.47 on 8 df, p=0.9633
Score (logrank) test = 4.97 on 8 df, p=0.7604
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 1 rows containing missing values (geom_errorbar).Removed 9 rows containing missing values (geom_text).Removed 9 rows containing missing values (geom_text).Removed 9 rows containing missing values (geom_text).Removed 9 rows containing missing values (geom_text).Removed 9 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).
## Unadjusted Kaplan Meier Overall Survival Curve for: FACILITY_LOCATION_F
Facility Geography
uni_var(test_var = "FACILITY_GEOGRAPHY", data_imp = data)
_________________________________________________
## FACILITY_GEOGRAPHY
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_GEOGRAPHY, data = data)
11 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
FACILITY_GEOGRAPHY=Northeast 9 7 12.2 7.49 NA
FACILITY_GEOGRAPHY=South 10 4 NA 12.09 NA
FACILITY_GEOGRAPHY=Midwest 10 6 18.8 2.20 NA
FACILITY_GEOGRAPHY=West 8 4 14.2 2.04 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_GEOGRAPHY, data = data)
11 observations deleted due to missingness
FACILITY_GEOGRAPHY=Northeast
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 5 4 0.556 0.166 0.3097 0.997
24 2 3 0.222 0.139 0.0655 0.754
36 2 0 0.222 0.139 0.0655 0.754
48 1 0 0.222 0.139 0.0655 0.754
60 1 0 0.222 0.139 0.0655 0.754
FACILITY_GEOGRAPHY=South
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 7 2 0.800 0.126 0.587 1
24 4 2 0.549 0.172 0.296 1
36 3 0 0.549 0.172 0.296 1
48 3 0 0.549 0.172 0.296 1
FACILITY_GEOGRAPHY=Midwest
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 6 4 0.600 0.155 0.362 0.995
24 3 2 0.375 0.161 0.162 0.868
36 2 0 0.375 0.161 0.162 0.868
48 1 0 0.375 0.161 0.162 0.868
60 1 0 0.375 0.161 0.162 0.868
FACILITY_GEOGRAPHY=West
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 3 3 0.571 0.187 0.301 1
24 1 1 0.381 0.199 0.137 1
36 1 0 0.381 0.199 0.137 1
48 1 0 0.381 0.199 0.137 1
60 1 0 0.381 0.199 0.137 1
## Univariable Cox Proportional Hazard Model for: FACILITY_GEOGRAPHY
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ FACILITY_GEOGRAPHY, data = data)
n= 37, number of events= 21
(11 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
FACILITY_GEOGRAPHYSouth -0.7310 0.4814 0.6296 -1.161 0.246
FACILITY_GEOGRAPHYMidwest -0.2676 0.7652 0.5575 -0.480 0.631
FACILITY_GEOGRAPHYWest -0.1715 0.8424 0.6278 -0.273 0.785
exp(coef) exp(-coef) lower .95 upper .95
FACILITY_GEOGRAPHYSouth 0.4814 2.077 0.1402 1.654
FACILITY_GEOGRAPHYMidwest 0.7652 1.307 0.2566 2.282
FACILITY_GEOGRAPHYWest 0.8424 1.187 0.2461 2.883
Concordance= 0.555 (se = 0.067 )
Rsquare= 0.039 (max possible= 0.972 )
Likelihood ratio test= 1.48 on 3 df, p=0.6869
Wald test = 1.37 on 3 df, p=0.7114
Score (logrank) test = 1.42 on 3 df, p=0.7015
Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: FACILITY_GEOGRAPHY
Age Group
uni_var(test_var = "AGE_F", data_imp = data)
_________________________________________________
## AGE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ AGE_F, data = data)
n events median 0.95LCL 0.95UCL
AGE_F=(0,54] 22 10 NA 12.19 NA
AGE_F=(54,64] 12 5 NA 3.15 NA
AGE_F=(64,74] 5 4 8.08 7.49 NA
AGE_F=(74,100] 9 7 7.14 2.20 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ AGE_F, data = data)
AGE_F=(0,54]
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 15 7 0.682 0.0993 0.513 0.907
24 12 3 0.545 0.1062 0.372 0.799
36 8 0 0.545 0.1062 0.372 0.799
48 6 0 0.545 0.1062 0.372 0.799
60 2 0 0.545 0.1062 0.372 0.799
AGE_F=(54,64]
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 6 4 0.648 0.143 0.421 0.998
24 4 1 0.519 0.163 0.280 0.959
36 4 0 0.519 0.163 0.280 0.959
48 3 0 0.519 0.163 0.280 0.959
60 2 0 0.519 0.163 0.280 0.959
AGE_F=(64,74]
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12.000 2.000 3.000 0.400 0.219 0.137 1.000
AGE_F=(74,100]
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12.000 4.000 4.000 0.500 0.177 0.250 1.000
## Univariable Cox Proportional Hazard Model for: AGE_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ AGE_F, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
AGE_F(54,64] 0.2168 1.2421 0.5494 0.395 0.693
AGE_F(64,74] 0.9213 2.5124 0.6018 1.531 0.126
AGE_F(74,100] 1.1714 3.2266 0.5075 2.308 0.021 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
AGE_F(54,64] 1.242 0.8051 0.4231 3.646
AGE_F(64,74] 2.512 0.3980 0.7724 8.172
AGE_F(74,100] 3.227 0.3099 1.1932 8.725
Concordance= 0.611 (se = 0.055 )
Rsquare= 0.116 (max possible= 0.976 )
Likelihood ratio test= 5.91 on 3 df, p=0.1161
Wald test = 6.33 on 3 df, p=0.09657
Score (logrank) test = 6.91 on 3 df, p=0.0748
Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: AGE_F
Age Group
uni_var(test_var = "AGE_40", data_imp = data)
_________________________________________________
## AGE_40
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ AGE_40, data = data)
n events median 0.95LCL 0.95UCL
AGE_40=(0,40] 12 6 22.6 4.24 NA
AGE_40=(40,100] 36 20 16.4 8.08 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ AGE_40, data = data)
AGE_40=(0,40]
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 7 5 0.583 0.142 0.362 0.941
24 6 1 0.500 0.144 0.284 0.880
36 4 0 0.500 0.144 0.284 0.880
48 3 0 0.500 0.144 0.284 0.880
60 1 0 0.500 0.144 0.284 0.880
AGE_40=(40,100]
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 20 13 0.621 0.0830 0.478 0.807
24 10 7 0.386 0.0877 0.247 0.602
36 8 0 0.386 0.0877 0.247 0.602
48 6 0 0.386 0.0877 0.247 0.602
60 3 0 0.386 0.0877 0.247 0.602
## Univariable Cox Proportional Hazard Model for: AGE_40
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ AGE_40, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
AGE_40(40,100] 0.219 1.245 0.467 0.469 0.639
exp(coef) exp(-coef) lower .95 upper .95
AGE_40(40,100] 1.245 0.8033 0.4984 3.109
Concordance= 0.511 (se = 0.046 )
Rsquare= 0.005 (max possible= 0.976 )
Likelihood ratio test= 0.23 on 1 df, p=0.6327
Wald test = 0.22 on 1 df, p=0.6391
Score (logrank) test = 0.22 on 1 df, p=0.6385
Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: AGE_40
Gender
uni_var(test_var = "SEX_F", data_imp = data)
_________________________________________________
## SEX_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SEX_F, data = data)
n events median 0.95LCL 0.95UCL
SEX_F=Male 30 18 14.2 4.76 NA
SEX_F=Female 18 8 NA 12.19 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SEX_F, data = data)
SEX_F=Male
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 15 13 0.556 0.0921 0.401 0.769
24 7 5 0.343 0.0949 0.199 0.590
36 5 0 0.343 0.0949 0.199 0.590
48 2 0 0.343 0.0949 0.199 0.590
60 2 0 0.343 0.0949 0.199 0.590
SEX_F=Female
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 12 5 0.706 0.111 0.519 0.959
24 9 3 0.529 0.121 0.338 0.829
36 7 0 0.529 0.121 0.338 0.829
48 7 0 0.529 0.121 0.338 0.829
60 2 0 0.529 0.121 0.338 0.829
## Univariable Cox Proportional Hazard Model for: SEX_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SEX_F, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
SEX_FFemale -0.4865 0.6148 0.4263 -1.141 0.254
exp(coef) exp(-coef) lower .95 upper .95
SEX_FFemale 0.6148 1.627 0.2666 1.418
Concordance= 0.557 (se = 0.051 )
Rsquare= 0.028 (max possible= 0.976 )
Likelihood ratio test= 1.37 on 1 df, p=0.2411
Wald test = 1.3 on 1 df, p=0.2538
Score (logrank) test = 1.33 on 1 df, p=0.2492
Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: SEX_F
RACE_F
uni_var(test_var = "RACE_F", data_imp = data)
_________________________________________________
## RACE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RACE_F, data = data)
n events median 0.95LCL 0.95UCL
RACE_F=White 38 21 18.73 8.08 NA
RACE_F=Black 7 4 16.36 2.50 NA
RACE_F=Other/Unk 2 1 4.76 4.76 NA
RACE_F=Asian 1 0 NA NA NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RACE_F, data = data)
RACE_F=White
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 21 14 0.614 0.0809 0.475 0.795
24 12 7 0.395 0.0848 0.260 0.602
36 9 0 0.395 0.0848 0.260 0.602
48 6 0 0.395 0.0848 0.260 0.602
60 3 0 0.395 0.0848 0.260 0.602
RACE_F=Black
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 4 3 0.571 0.187 0.301 1
24 3 1 0.429 0.187 0.182 1
36 2 0 0.429 0.187 0.182 1
48 2 0 0.429 0.187 0.182 1
RACE_F=Other/Unk
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 1 0.5 0.354 0.125 1
24 1 0 0.5 0.354 0.125 1
36 1 0 0.5 0.354 0.125 1
48 1 0 0.5 0.354 0.125 1
60 1 0 0.5 0.354 0.125 1
RACE_F=Asian
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 0 1 0 1 1
## Univariable Cox Proportional Hazard Model for: RACE_F
Loglik converged before variable 3 ; beta may be infinite.
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RACE_F, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
RACE_FBlack -3.730e-02 9.634e-01 5.458e-01 -0.068 0.946
RACE_FOther/Unk -2.627e-01 7.690e-01 1.024e+00 -0.256 0.798
RACE_FAsian -1.707e+01 3.845e-08 5.574e+03 -0.003 0.998
exp(coef) exp(-coef) lower .95 upper .95
RACE_FBlack 9.634e-01 1.038e+00 0.3305 2.808
RACE_FOther/Unk 7.690e-01 1.300e+00 0.1033 5.724
RACE_FAsian 3.845e-08 2.600e+07 0.0000 Inf
Concordance= 0.521 (se = 0.044 )
Rsquare= 0.035 (max possible= 0.976 )
Likelihood ratio test= 1.69 on 3 df, p=0.6391
Wald test = 0.07 on 3 df, p=0.9954
Score (logrank) test = 0.89 on 3 df, p=0.8271
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 1 rows containing missing values (geom_errorbar).Removed 4 rows containing missing values (geom_text).Removed 4 rows containing missing values (geom_text).Removed 4 rows containing missing values (geom_text).Removed 4 rows containing missing values (geom_text).Removed 4 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).
## Unadjusted Kaplan Meier Overall Survival Curve for: RACE_F
Hispanic
uni_var(test_var = "HISPANIC", data_imp = data)
_________________________________________________
## HISPANIC
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ HISPANIC, data = data)
n events median 0.95LCL 0.95UCL
HISPANIC=No 43 25 16.36 7.49 NA
HISPANIC=Yes 3 0 NA NA NA
HISPANIC=Unknown 2 1 4.76 4.76 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ HISPANIC, data = data)
HISPANIC=No
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 23 17 0.588 0.0767 0.456 0.76
24 12 8 0.369 0.0785 0.243 0.56
36 10 0 0.369 0.0785 0.243 0.56
48 7 0 0.369 0.0785 0.243 0.56
60 3 0 0.369 0.0785 0.243 0.56
HISPANIC=Yes
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 3 0 1 0 1 1
24 3 0 1 0 1 1
36 2 0 1 0 1 1
48 2 0 1 0 1 1
60 1 0 1 0 1 1
HISPANIC=Unknown
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 1 0.5 0.354 0.125 1
24 1 0 0.5 0.354 0.125 1
## Univariable Cox Proportional Hazard Model for: HISPANIC
Loglik converged before variable 1 ; beta may be infinite.
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ HISPANIC, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
HISPANICYes -1.820e+01 1.246e-08 5.245e+03 -0.003 0.997
HISPANICUnknown -3.352e-01 7.152e-01 1.021e+00 -0.328 0.743
exp(coef) exp(-coef) lower .95 upper .95
HISPANICYes 1.246e-08 8.028e+07 0.00000 Inf
HISPANICUnknown 7.152e-01 1.398e+00 0.09678 5.285
Concordance= 0.554 (se = 0.036 )
Rsquare= 0.109 (max possible= 0.976 )
Likelihood ratio test= 5.54 on 2 df, p=0.06266
Wald test = 0.11 on 2 df, p=0.9475
Score (logrank) test = 2.99 on 2 df, p=0.2242
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 1 rows containing missing values (geom_errorbar).Removed 3 rows containing missing values (geom_text).Removed 3 rows containing missing values (geom_text).Removed 3 rows containing missing values (geom_text).Removed 3 rows containing missing values (geom_text).Removed 3 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).
## Unadjusted Kaplan Meier Overall Survival Curve for: HISPANIC
Insurance Status
uni_var(test_var = "INSURANCE_F", data_imp = data)
_________________________________________________
## INSURANCE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ INSURANCE_F, data = data)
n events median 0.95LCL 0.95UCL
INSURANCE_F=Private 29 12 NA 16.36 NA
INSURANCE_F=None 2 2 3.19 0.23 NA
INSURANCE_F=Medicaid 3 2 4.76 0.66 NA
INSURANCE_F=Medicare 12 8 12.09 2.50 NA
INSURANCE_F=Other Government 1 1 14.16 NA NA
INSURANCE_F=Unknown 1 1 4.30 NA NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ INSURANCE_F, data = data)
INSURANCE_F=Private
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 20 8 0.718 0.0845 0.570 0.905
24 14 4 0.564 0.0955 0.405 0.786
36 11 0 0.564 0.0955 0.405 0.786
48 8 0 0.564 0.0955 0.405 0.786
60 4 0 0.564 0.0955 0.405 0.786
INSURANCE_F=None
time n.risk n.event survival std.err lower 95% CI upper 95% CI
INSURANCE_F=Medicaid
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 2 0.333 0.272 0.0673 1
24 1 0 0.333 0.272 0.0673 1
INSURANCE_F=Medicare
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 5 5 0.545 0.150 0.3180 0.936
24 1 3 0.218 0.134 0.0656 0.725
36 1 0 0.218 0.134 0.0656 0.725
48 1 0 0.218 0.134 0.0656 0.725
INSURANCE_F=Other Government
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 0 1 0 1 1
INSURANCE_F=Unknown
time n.risk n.event survival std.err lower 95% CI upper 95% CI
## Univariable Cox Proportional Hazard Model for: INSURANCE_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ INSURANCE_F, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
INSURANCE_FNone 2.0407 7.6958 0.7942 2.569 0.0102 *
INSURANCE_FMedicaid 0.8280 2.2888 0.7657 1.081 0.2795
INSURANCE_FMedicare 0.8366 2.3086 0.4635 1.805 0.0710 .
INSURANCE_FOther Government 0.9571 2.6042 1.0511 0.911 0.3625
INSURANCE_FUnknown 1.5670 4.7924 1.0678 1.468 0.1422
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
INSURANCE_FNone 7.696 0.1299 1.6226 36.502
INSURANCE_FMedicaid 2.289 0.4369 0.5103 10.266
INSURANCE_FMedicare 2.309 0.4332 0.9308 5.726
INSURANCE_FOther Government 2.604 0.3840 0.3319 20.434
INSURANCE_FUnknown 4.792 0.2087 0.5911 38.855
Concordance= 0.616 (se = 0.051 )
Rsquare= 0.152 (max possible= 0.976 )
Likelihood ratio test= 7.92 on 5 df, p=0.1608
Wald test = 9.1 on 5 df, p=0.1052
Score (logrank) test = 10.87 on 5 df, p=0.05407
Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: INSURANCE_F
Overall Survival pre/post-ACA expansion
uni_var(test_var = "EXPN_GROUP", data_imp = no_Excludes)
_________________________________________________
## EXPN_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ EXPN_GROUP, data = no_Excludes)
n events median 0.95LCL 0.95UCL
EXPN_GROUP=Post-Expansion 248 36 NA 82.8 NA
EXPN_GROUP=Pre-Expansion 1137 303 142 128.2 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ EXPN_GROUP, data = no_Excludes)
EXPN_GROUP=Post-Expansion
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 206 8 0.965 0.0121 0.942 0.989
24 144 16 0.882 0.0229 0.838 0.928
36 79 6 0.838 0.0280 0.785 0.894
48 52 3 0.797 0.0351 0.731 0.869
60 33 1 0.780 0.0384 0.708 0.859
EXPN_GROUP=Pre-Expansion
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1000 41 0.962 0.00580 0.951 0.974
24 900 41 0.921 0.00835 0.905 0.938
36 799 34 0.885 0.01009 0.865 0.905
48 683 33 0.846 0.01167 0.824 0.870
60 568 44 0.789 0.01376 0.762 0.816
120 125 94 0.586 0.02213 0.545 0.631
## Univariable Cox Proportional Hazard Model for: EXPN_GROUP
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ EXPN_GROUP, data = no_Excludes)
n= 1385, number of events= 339
coef exp(coef) se(coef) z Pr(>|z|)
EXPN_GROUPPre-Expansion -0.2137 0.8076 0.1815 -1.178 0.239
exp(coef) exp(-coef) lower .95 upper .95
EXPN_GROUPPre-Expansion 0.8076 1.238 0.5659 1.153
Concordance= 0.514 (se = 0.01 )
Rsquare= 0.001 (max possible= 0.958 )
Likelihood ratio test= 1.32 on 1 df, p=0.2507
Wald test = 1.39 on 1 df, p=0.239
Score (logrank) test = 1.39 on 1 df, p=0.2381
## Unadjusted Kaplan Meier Overall Survival Curve for: EXPN_GROUP
Education
uni_var(test_var = "EDUCATION_F", data_imp = data)
_________________________________________________
## EDUCATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ EDUCATION_F, data = data)
n events median 0.95LCL 0.95UCL
EDUCATION_F=21% or more 6 3 12.2 1.58 NA
EDUCATION_F=13 - 20.9% 14 10 12.1 4.76 NA
EDUCATION_F=7 - 12.9% 15 6 NA 7.49 NA
EDUCATION_F=Less than 7% 13 7 21.3 2.50 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ EDUCATION_F, data = data)
EDUCATION_F=21% or more
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 3 2 0.667 0.192 0.379 1
24 2 1 0.444 0.222 0.167 1
36 2 0 0.444 0.222 0.167 1
48 2 0 0.444 0.222 0.167 1
EDUCATION_F=13 - 20.9%
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 7 6 0.538 0.138 0.3255 0.891
24 3 4 0.231 0.117 0.0855 0.623
36 2 0 0.231 0.117 0.0855 0.623
48 2 0 0.231 0.117 0.0855 0.623
60 1 0 0.231 0.117 0.0855 0.623
EDUCATION_F=7 - 12.9%
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 10 5 0.667 0.122 0.466 0.953
24 7 1 0.593 0.129 0.387 0.907
36 5 0 0.593 0.129 0.387 0.907
48 4 0 0.593 0.129 0.387 0.907
60 2 0 0.593 0.129 0.387 0.907
EDUCATION_F=Less than 7%
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 7 5 0.598 0.140 0.378 0.947
24 4 2 0.399 0.148 0.193 0.826
36 3 0 0.399 0.148 0.193 0.826
48 1 0 0.399 0.148 0.193 0.826
60 1 0 0.399 0.148 0.193 0.826
## Univariable Cox Proportional Hazard Model for: EDUCATION_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ EDUCATION_F, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
EDUCATION_F13 - 20.9% 0.41117 1.50858 0.65908 0.624 0.533
EDUCATION_F7 - 12.9% -0.50351 0.60441 0.70840 -0.711 0.477
EDUCATION_FLess than 7% 0.04348 1.04444 0.69088 0.063 0.950
exp(coef) exp(-coef) lower .95 upper .95
EDUCATION_F13 - 20.9% 1.5086 0.6629 0.4145 5.490
EDUCATION_F7 - 12.9% 0.6044 1.6545 0.1508 2.423
EDUCATION_FLess than 7% 1.0444 0.9575 0.2697 4.045
Concordance= 0.591 (se = 0.058 )
Rsquare= 0.066 (max possible= 0.976 )
Likelihood ratio test= 3.27 on 3 df, p=0.3519
Wald test = 3.12 on 3 df, p=0.3741
Score (logrank) test = 3.27 on 3 df, p=0.3517
Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: EDUCATION_F
Urban/Rural
uni_var(test_var = "U_R_F", data_imp = data)
_________________________________________________
## U_R_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ U_R_F, data = data)
n events median 0.95LCL 0.95UCL
U_R_F=Metro 39 22 18.73 7.49 NA
U_R_F=Urban 8 3 NA 8.08 NA
U_R_F=Rural 1 1 3.15 NA NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ U_R_F, data = data)
U_R_F=Metro
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 23 14 0.625 0.0793 0.488 0.802
24 12 8 0.392 0.0826 0.259 0.592
36 10 0 0.392 0.0826 0.259 0.592
48 7 0 0.392 0.0826 0.259 0.592
60 4 0 0.392 0.0826 0.259 0.592
U_R_F=Urban
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 4 3 0.625 0.171 0.365 1
24 4 0 0.625 0.171 0.365 1
36 2 0 0.625 0.171 0.365 1
48 2 0 0.625 0.171 0.365 1
U_R_F=Rural
time n.risk n.event survival std.err lower 95% CI upper 95% CI
## Univariable Cox Proportional Hazard Model for: U_R_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ U_R_F, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
U_R_FUrban -0.5878 0.5556 0.6162 -0.954 0.340
U_R_FRural 1.1896 3.2859 1.0477 1.135 0.256
exp(coef) exp(-coef) lower .95 upper .95
U_R_FUrban 0.5556 1.8000 0.1661 1.859
U_R_FRural 3.2859 0.3043 0.4215 25.614
Concordance= 0.552 (se = 0.043 )
Rsquare= 0.043 (max possible= 0.976 )
Likelihood ratio test= 2.13 on 2 df, p=0.3448
Wald test = 2.37 on 2 df, p=0.3057
Score (logrank) test = 2.64 on 2 df, p=0.2671
Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: U_R_F
Year
uni_var(test_var = "YEAR_OF_DIAGNOSIS", data_imp = data)
_________________________________________________
## YEAR_OF_DIAGNOSIS
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ YEAR_OF_DIAGNOSIS, data = data)
n events median 0.95LCL 0.95UCL
YEAR_OF_DIAGNOSIS=2010 4 1 NA 2.04 NA
YEAR_OF_DIAGNOSIS=2011 3 1 NA 14.16 NA
YEAR_OF_DIAGNOSIS=2012 9 7 12.09 4.24 NA
YEAR_OF_DIAGNOSIS=2013 10 4 NA 7.49 NA
YEAR_OF_DIAGNOSIS=2014 12 7 6.14 3.15 NA
YEAR_OF_DIAGNOSIS=2015 10 6 12.19 2.50 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ YEAR_OF_DIAGNOSIS, data = data)
YEAR_OF_DIAGNOSIS=2010
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 3 1 0.75 0.217 0.426 1
24 3 0 0.75 0.217 0.426 1
36 3 0 0.75 0.217 0.426 1
48 3 0 0.75 0.217 0.426 1
60 3 0 0.75 0.217 0.426 1
YEAR_OF_DIAGNOSIS=2011
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 3 0 1.000 0.000 1.0 1
24 2 1 0.667 0.272 0.3 1
36 2 0 0.667 0.272 0.3 1
48 1 0 0.667 0.272 0.3 1
YEAR_OF_DIAGNOSIS=2012
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 5 4 0.556 0.166 0.3097 0.997
24 2 3 0.222 0.139 0.0655 0.754
36 2 0 0.222 0.139 0.0655 0.754
48 2 0 0.222 0.139 0.0655 0.754
60 1 0 0.222 0.139 0.0655 0.754
YEAR_OF_DIAGNOSIS=2013
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 7 3 0.700 0.145 0.467 1
24 5 1 0.583 0.161 0.340 1
36 5 0 0.583 0.161 0.340 1
48 3 0 0.583 0.161 0.340 1
YEAR_OF_DIAGNOSIS=2014
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 4 6 0.416 0.156 0.199 0.868
24 3 1 0.312 0.148 0.123 0.789
YEAR_OF_DIAGNOSIS=2015
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 5 4 0.60 0.155 0.3617 0.995
24 1 2 0.24 0.188 0.0515 1.000
## Univariable Cox Proportional Hazard Model for: YEAR_OF_DIAGNOSIS
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ YEAR_OF_DIAGNOSIS, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
YEAR_OF_DIAGNOSIS2011 0.1752 1.1914 1.4147 0.124 0.901
YEAR_OF_DIAGNOSIS2012 1.4528 4.2751 1.0712 1.356 0.175
YEAR_OF_DIAGNOSIS2013 0.5911 1.8060 1.1185 0.528 0.597
YEAR_OF_DIAGNOSIS2014 1.4031 4.0680 1.0716 1.309 0.190
YEAR_OF_DIAGNOSIS2015 1.1273 3.0872 1.0832 1.041 0.298
exp(coef) exp(-coef) lower .95 upper .95
YEAR_OF_DIAGNOSIS2011 1.191 0.8393 0.07445 19.07
YEAR_OF_DIAGNOSIS2012 4.275 0.2339 0.52379 34.89
YEAR_OF_DIAGNOSIS2013 1.806 0.5537 0.20166 16.17
YEAR_OF_DIAGNOSIS2014 4.068 0.2458 0.49800 33.23
YEAR_OF_DIAGNOSIS2015 3.087 0.3239 0.36947 25.80
Concordance= 0.617 (se = 0.06 )
Rsquare= 0.106 (max possible= 0.976 )
Likelihood ratio test= 5.35 on 5 df, p=0.3743
Wald test = 4.55 on 5 df, p=0.4735
Score (logrank) test = 4.99 on 5 df, p=0.4169
Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: YEAR_OF_DIAGNOSIS
Primary Site
uni_var(test_var = "SITE_TEXT", data_imp = data)
_________________________________________________
## SITE_TEXT
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SITE_TEXT, data = data)
n events median 0.95LCL 0.95UCL
SITE_TEXT=C44.3 Skin of ear and unspecified parts of face 2 0 NA NA NA
SITE_TEXT=C44.5 Skin of trunk 4 1 NA 2.04 NA
SITE_TEXT=C44.6 Skin of upper limb and shoulder 2 1 6.14 6.14 NA
SITE_TEXT=C44.7 Skin of lower limb and hip 11 7 18.73 7.49 NA
SITE_TEXT=C44.8 Overlapping lesion of skin 3 1 NA 14.16 NA
SITE_TEXT=C44.9 Skin, NOS 26 16 10.13 2.50 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SITE_TEXT, data = data)
SITE_TEXT=C44.3 Skin of ear and unspecified parts of face
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 0 1 0 1 1
24 1 0 1 0 1 1
SITE_TEXT=C44.5 Skin of trunk
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 1 0.667 0.272 0.3 1
24 1 0 0.667 0.272 0.3 1
36 1 0 0.667 0.272 0.3 1
48 1 0 0.667 0.272 0.3 1
60 1 0 0.667 0.272 0.3 1
SITE_TEXT=C44.6 Skin of upper limb and shoulder
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 1 0.5 0.354 0.125 1
24 1 0 0.5 0.354 0.125 1
SITE_TEXT=C44.7 Skin of lower limb and hip
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 7 3 0.70 0.145 0.4665 1.000
24 2 4 0.24 0.145 0.0732 0.786
36 2 0 0.24 0.145 0.0732 0.786
48 1 0 0.24 0.145 0.0732 0.786
SITE_TEXT=C44.8 Overlapping lesion of skin
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 3 0 1.000 0.000 1.0 1
24 2 1 0.667 0.272 0.3 1
36 2 0 0.667 0.272 0.3 1
48 2 0 0.667 0.272 0.3 1
SITE_TEXT=C44.9 Skin, NOS
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 13 13 0.500 0.0981 0.340 0.734
24 9 3 0.381 0.0960 0.232 0.624
36 7 0 0.381 0.0960 0.232 0.624
48 5 0 0.381 0.0960 0.232 0.624
60 3 0 0.381 0.0960 0.232 0.624
## Univariable Cox Proportional Hazard Model for: SITE_TEXT
Loglik converged before variable 1,2,3,4,5 ; beta may be infinite.
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SITE_TEXT, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
SITE_TEXTC44.5 Skin of trunk 1.756e+01 4.241e+07 6.549e+03 0.003 0.998
SITE_TEXTC44.6 Skin of upper limb and shoulder 1.784e+01 5.593e+07 6.549e+03 0.003 0.998
SITE_TEXTC44.7 Skin of lower limb and hip 1.824e+01 8.369e+07 6.549e+03 0.003 0.998
SITE_TEXTC44.8 Overlapping lesion of skin 1.724e+01 3.067e+07 6.549e+03 0.003 0.998
SITE_TEXTC44.9 Skin, NOS 1.828e+01 8.666e+07 6.549e+03 0.003 0.998
exp(coef) exp(-coef) lower .95 upper .95
SITE_TEXTC44.5 Skin of trunk 42408559 2.358e-08 0 Inf
SITE_TEXTC44.6 Skin of upper limb and shoulder 55932132 1.788e-08 0 Inf
SITE_TEXTC44.7 Skin of lower limb and hip 83685141 1.195e-08 0 Inf
SITE_TEXTC44.8 Overlapping lesion of skin 30672307 3.260e-08 0 Inf
SITE_TEXTC44.9 Skin, NOS 86656240 1.154e-08 0 Inf
Concordance= 0.606 (se = 0.056 )
Rsquare= 0.106 (max possible= 0.976 )
Likelihood ratio test= 5.4 on 5 df, p=0.3689
Wald test = 1.56 on 5 df, p=0.906
Score (logrank) test = 3.55 on 5 df, p=0.6159
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 1 rows containing missing values (geom_errorbar).Removed 6 rows containing missing values (geom_text).Removed 6 rows containing missing values (geom_text).Removed 6 rows containing missing values (geom_text).Removed 6 rows containing missing values (geom_text).Removed 6 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).
## Unadjusted Kaplan Meier Overall Survival Curve for: SITE_TEXT
Histology
#uni_var(test_var = "HISTOLOGY_F_LIM", data_imp = data)
Grade
#uni_var(test_var = "GRADE_F", data_imp = data)
Clinical T Stage
uni_var(test_var = "TNM_CLIN_T", data_imp = data)
_________________________________________________
## TNM_CLIN_T
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_CLIN_T, data = data)
4 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
44.00 26.00 16.36 7.49 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_CLIN_T, data = data)
4 observations deleted due to missingness
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 25 18 0.584 0.0751 0.454 0.751
24 15 8 0.387 0.0757 0.263 0.567
36 12 0 0.387 0.0757 0.263 0.567
48 9 0 0.387 0.0757 0.263 0.567
60 4 0 0.387 0.0757 0.263 0.567
## Univariable Cox Proportional Hazard Model for: TNM_CLIN_T
[1] "Only one level, no Cox model performed"
## Unadjusted Kaplan Meier Overall Survival Curve for: TNM_CLIN_T
Clinical N Stage
uni_var(test_var = "TNM_CLIN_N", data_imp = data)
_________________________________________________
## TNM_CLIN_N
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_CLIN_N, data = data)
4 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
44.00 26.00 16.36 7.49 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_CLIN_N, data = data)
4 observations deleted due to missingness
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 25 18 0.584 0.0751 0.454 0.751
24 15 8 0.387 0.0757 0.263 0.567
36 12 0 0.387 0.0757 0.263 0.567
48 9 0 0.387 0.0757 0.263 0.567
60 4 0 0.387 0.0757 0.263 0.567
## Univariable Cox Proportional Hazard Model for: TNM_CLIN_N
[1] "Only one level, no Cox model performed"
## Unadjusted Kaplan Meier Overall Survival Curve for: TNM_CLIN_N
Clinical Stage Group
uni_var(test_var = "TNM_CLIN_STAGE_GROUP", data_imp = data)
_________________________________________________
## TNM_CLIN_STAGE_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_CLIN_STAGE_GROUP, data = data)
n events median 0.95LCL 0.95UCL
TNM_CLIN_STAGE_GROUP=1 11 3 NA 21.29 NA
TNM_CLIN_STAGE_GROUP=1A 8 5 18.73 12.09 NA
TNM_CLIN_STAGE_GROUP=1B 4 1 NA 4.30 NA
TNM_CLIN_STAGE_GROUP=2B 3 1 23.95 23.95 NA
TNM_CLIN_STAGE_GROUP=3 2 1 14.16 14.16 NA
TNM_CLIN_STAGE_GROUP=3B 1 1 2.04 NA NA
TNM_CLIN_STAGE_GROUP=4 3 3 4.24 3.15 NA
TNM_CLIN_STAGE_GROUP=4A 8 6 2.35 0.66 NA
TNM_CLIN_STAGE_GROUP=4B 4 2 12.19 2.20 NA
TNM_CLIN_STAGE_GROUP=99 4 3 3.86 1.51 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_CLIN_STAGE_GROUP, data = data)
TNM_CLIN_STAGE_GROUP=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 8 2 0.800 0.126 0.587 1
24 5 1 0.667 0.161 0.415 1
36 3 0 0.667 0.161 0.415 1
48 2 0 0.667 0.161 0.415 1
TNM_CLIN_STAGE_GROUP=1A
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 6 1 0.857 0.132 0.6334 1.000
24 2 4 0.286 0.171 0.0886 0.922
36 2 0 0.286 0.171 0.0886 0.922
48 1 0 0.286 0.171 0.0886 0.922
60 1 0 0.286 0.171 0.0886 0.922
TNM_CLIN_STAGE_GROUP=1B
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 3 1 0.75 0.217 0.426 1
24 3 0 0.75 0.217 0.426 1
36 2 0 0.75 0.217 0.426 1
48 2 0 0.75 0.217 0.426 1
60 1 0 0.75 0.217 0.426 1
TNM_CLIN_STAGE_GROUP=2B
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 0 1.0 0.000 1.000 1
24 1 1 0.5 0.354 0.125 1
36 1 0 0.5 0.354 0.125 1
48 1 0 0.5 0.354 0.125 1
TNM_CLIN_STAGE_GROUP=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 0 1.0 0.000 1.000 1
24 1 1 0.5 0.354 0.125 1
36 1 0 0.5 0.354 0.125 1
48 1 0 0.5 0.354 0.125 1
60 1 0 0.5 0.354 0.125 1
TNM_CLIN_STAGE_GROUP=3B
time n.risk n.event survival std.err lower 95% CI upper 95% CI
TNM_CLIN_STAGE_GROUP=4
time n.risk n.event survival std.err lower 95% CI upper 95% CI
TNM_CLIN_STAGE_GROUP=4A
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 6 0.25 0.153 0.0753 0.83
24 1 0 0.25 0.153 0.0753 0.83
36 1 0 0.25 0.153 0.0753 0.83
48 1 0 0.25 0.153 0.0753 0.83
60 1 0 0.25 0.153 0.0753 0.83
TNM_CLIN_STAGE_GROUP=4B
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 3 1 0.75 0.217 0.426 1
24 2 1 0.50 0.250 0.188 1
36 2 0 0.50 0.250 0.188 1
48 1 0 0.50 0.250 0.188 1
TNM_CLIN_STAGE_GROUP=99
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 3 0.25 0.217 0.0458 1
24 1 0 0.25 0.217 0.0458 1
## Univariable Cox Proportional Hazard Model for: TNM_CLIN_STAGE_GROUP
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_CLIN_STAGE_GROUP, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
TNM_CLIN_STAGE_GROUP1A 0.79183 2.20743 0.73182 1.082 0.2792
TNM_CLIN_STAGE_GROUP1B -0.31102 0.73270 1.15523 -0.269 0.7878
TNM_CLIN_STAGE_GROUP2B -0.02213 0.97811 1.15517 -0.019 0.9847
TNM_CLIN_STAGE_GROUP3 0.38805 1.47411 1.15543 0.336 0.7370
TNM_CLIN_STAGE_GROUP3B 2.76818 15.92967 1.21635 2.276 0.0229 *
TNM_CLIN_STAGE_GROUP4 2.07853 7.99271 0.85897 2.420 0.0155 *
TNM_CLIN_STAGE_GROUP4A 1.58515 4.88003 0.71180 2.227 0.0260 *
TNM_CLIN_STAGE_GROUP4B 0.55885 1.74866 0.91346 0.612 0.5407
TNM_CLIN_STAGE_GROUP99 1.49164 4.44436 0.82162 1.815 0.0695 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TNM_CLIN_STAGE_GROUP1A 2.2074 0.45302 0.52597 9.264
TNM_CLIN_STAGE_GROUP1B 0.7327 1.36482 0.07614 7.051
TNM_CLIN_STAGE_GROUP2B 0.9781 1.02238 0.10165 9.412
TNM_CLIN_STAGE_GROUP3 1.4741 0.67838 0.15312 14.192
TNM_CLIN_STAGE_GROUP3B 15.9297 0.06278 1.46842 172.807
TNM_CLIN_STAGE_GROUP4 7.9927 0.12511 1.48434 43.038
TNM_CLIN_STAGE_GROUP4A 4.8800 0.20492 1.20930 19.693
TNM_CLIN_STAGE_GROUP4B 1.7487 0.57187 0.29185 10.477
TNM_CLIN_STAGE_GROUP99 4.4444 0.22500 0.88806 22.242
Concordance= 0.712 (se = 0.06 )
Rsquare= 0.254 (max possible= 0.976 )
Likelihood ratio test= 14.05 on 9 df, p=0.1206
Wald test = 13.94 on 9 df, p=0.1246
Score (logrank) test = 17.28 on 9 df, p=0.04454
Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: TNM_CLIN_STAGE_GROUP
Pathologic T Stage
uni_var(test_var = "TNM_PATH_T", data_imp = data)
_________________________________________________
## TNM_PATH_T
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_T, data = data)
7 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
41.00 25.00 16.36 7.49 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_T, data = data)
7 observations deleted due to missingness
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 24 17 0.585 0.0769 0.452 0.757
24 14 8 0.379 0.0773 0.254 0.565
36 11 0 0.379 0.0773 0.254 0.565
48 8 0 0.379 0.0773 0.254 0.565
60 4 0 0.379 0.0773 0.254 0.565
## Univariable Cox Proportional Hazard Model for: TNM_PATH_T
[1] "Only one level, no Cox model performed"
## Unadjusted Kaplan Meier Overall Survival Curve for: TNM_PATH_T
Pathologic N Stage
uni_var(test_var = "TNM_PATH_N", data_imp = data)
_________________________________________________
## TNM_PATH_N
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_N, data = data)
7 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
41.00 25.00 16.36 7.49 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_N, data = data)
7 observations deleted due to missingness
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 24 17 0.585 0.0769 0.452 0.757
24 14 8 0.379 0.0773 0.254 0.565
36 11 0 0.379 0.0773 0.254 0.565
48 8 0 0.379 0.0773 0.254 0.565
60 4 0 0.379 0.0773 0.254 0.565
## Univariable Cox Proportional Hazard Model for: TNM_PATH_N
[1] "Only one level, no Cox model performed"
## Unadjusted Kaplan Meier Overall Survival Curve for: TNM_PATH_N
Pathologic M Stage
uni_var(test_var = "TNM_PATH_M", data_imp = data)
_________________________________________________
## TNM_PATH_M
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_M, data = data)
10 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
38.00 24.00 14.16 6.14 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_M, data = data)
10 observations deleted due to missingness
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 22 16 0.579 0.0801 0.441 0.759
24 12 8 0.355 0.0795 0.229 0.550
36 9 0 0.355 0.0795 0.229 0.550
48 6 0 0.355 0.0795 0.229 0.550
60 3 0 0.355 0.0795 0.229 0.550
## Univariable Cox Proportional Hazard Model for: TNM_PATH_M
[1] "Only one level, no Cox model performed"
## Unadjusted Kaplan Meier Overall Survival Curve for: TNM_PATH_M
Pathologic Stage Group
uni_var(test_var = "TNM_PATH_STAGE_GROUP", data_imp = data)
_________________________________________________
## TNM_PATH_STAGE_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_STAGE_GROUP, data = data)
15 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
TNM_PATH_STAGE_GROUP=1 3 3 0.66 0.23 NA
TNM_PATH_STAGE_GROUP=1B 2 0 NA NA NA
TNM_PATH_STAGE_GROUP=3 2 1 14.16 14.16 NA
TNM_PATH_STAGE_GROUP=3B 1 1 2.04 NA NA
TNM_PATH_STAGE_GROUP=4A 4 3 1.43 0.56 NA
TNM_PATH_STAGE_GROUP=99 21 12 16.36 8.08 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_STAGE_GROUP, data = data)
15 observations deleted due to missingness
TNM_PATH_STAGE_GROUP=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12.0000 1.0000 2.0000 0.3333 0.2722 0.0673 1.0000
TNM_PATH_STAGE_GROUP=1B
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 0 1 0 1 1
24 2 0 1 0 1 1
36 1 0 1 0 1 1
48 1 0 1 0 1 1
60 1 0 1 0 1 1
TNM_PATH_STAGE_GROUP=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 0 1.0 0.000 1.000 1
24 1 1 0.5 0.354 0.125 1
36 1 0 0.5 0.354 0.125 1
48 1 0 0.5 0.354 0.125 1
60 1 0 0.5 0.354 0.125 1
TNM_PATH_STAGE_GROUP=3B
time n.risk n.event survival std.err lower 95% CI upper 95% CI
TNM_PATH_STAGE_GROUP=4A
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 3 0.25 0.217 0.0458 1
24 1 0 0.25 0.217 0.0458 1
36 1 0 0.25 0.217 0.0458 1
48 1 0 0.25 0.217 0.0458 1
60 1 0 0.25 0.217 0.0458 1
TNM_PATH_STAGE_GROUP=99
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 12 7 0.635 0.110 0.452 0.892
24 6 5 0.353 0.113 0.189 0.661
36 5 0 0.353 0.113 0.189 0.661
48 3 0 0.353 0.113 0.189 0.661
60 1 0 0.353 0.113 0.189 0.661
## Univariable Cox Proportional Hazard Model for: TNM_PATH_STAGE_GROUP
Loglik converged before variable 1 ; beta may be infinite.
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ TNM_PATH_STAGE_GROUP, data = data)
n= 33, number of events= 20
(15 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TNM_PATH_STAGE_GROUP1B -1.953e+01 3.308e-09 6.862e+03 -0.003 0.9977
TNM_PATH_STAGE_GROUP3 -1.670e+00 1.883e-01 1.161e+00 -1.438 0.1504
TNM_PATH_STAGE_GROUP3B 4.768e-01 1.611e+00 1.204e+00 0.396 0.6922
TNM_PATH_STAGE_GROUP4A -4.192e-01 6.576e-01 8.263e-01 -0.507 0.6120
TNM_PATH_STAGE_GROUP99 -1.212e+00 2.976e-01 6.554e-01 -1.849 0.0644 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_STAGE_GROUP1B 3.308e-09 3.023e+08 0.00000 Inf
TNM_PATH_STAGE_GROUP3 1.883e-01 5.312e+00 0.01934 1.833
TNM_PATH_STAGE_GROUP3B 1.611e+00 6.208e-01 0.15206 17.065
TNM_PATH_STAGE_GROUP4A 6.576e-01 1.521e+00 0.13018 3.322
TNM_PATH_STAGE_GROUP99 2.976e-01 3.360e+00 0.08237 1.075
Concordance= 0.689 (se = 0.059 )
Rsquare= 0.256 (max possible= 0.974 )
Likelihood ratio test= 9.76 on 5 df, p=0.08225
Wald test = 6.29 on 5 df, p=0.2794
Score (logrank) test = 10.23 on 5 df, p=0.069
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 6 rows containing missing values (geom_text).Removed 6 rows containing missing values (geom_text).Removed 6 rows containing missing values (geom_text).Removed 6 rows containing missing values (geom_text).Removed 6 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).
## Unadjusted Kaplan Meier Overall Survival Curve for: TNM_PATH_STAGE_GROUP
Margins
uni_var(test_var = "MARGINS", data_imp = data)
_________________________________________________
## MARGINS
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ MARGINS, data = data)
n events median 0.95LCL 0.95UCL
MARGINS=No Residual 1 0 NA NA NA
MARGINS=Not evaluable 1 1 4.24 NA NA
MARGINS=No surg 42 24 18.73 8.08 NA
MARGINS=Unknown 4 1 NA 7.49 NA
Error in array(xx, dim = dd) : negative length vectors are not allowed
Margins Yes/No
#uni_var(test_var = "MARGINS_YN", data_imp = data)
30 Day Readmission
uni_var(test_var = "READM_HOSP_30_DAYS_F", data_imp = data)
_________________________________________________
## READM_HOSP_30_DAYS_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ READM_HOSP_30_DAYS_F, data = data)
n events median 0.95LCL 0.95UCL
READM_HOSP_30_DAYS_F=No_Surg_or_No_Readmit 47 25 21.29 7.49 NA
READM_HOSP_30_DAYS_F=9 1 1 8.08 NA NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ READM_HOSP_30_DAYS_F, data = data)
READM_HOSP_30_DAYS_F=No_Surg_or_No_Readmit
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 27 17 0.625 0.072 0.498 0.783
24 16 8 0.429 0.076 0.303 0.607
36 12 0 0.429 0.076 0.303 0.607
48 9 0 0.429 0.076 0.303 0.607
60 4 0 0.429 0.076 0.303 0.607
READM_HOSP_30_DAYS_F=9
time n.risk n.event survival std.err lower 95% CI upper 95% CI
## Univariable Cox Proportional Hazard Model for: READM_HOSP_30_DAYS_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ READM_HOSP_30_DAYS_F, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
READM_HOSP_30_DAYS_F9 0.7525 2.1222 1.0296 0.731 0.465
exp(coef) exp(-coef) lower .95 upper .95
READM_HOSP_30_DAYS_F9 2.122 0.4712 0.2821 15.97
Concordance= 0.507 (se = 0.016 )
Rsquare= 0.009 (max possible= 0.976 )
Likelihood ratio test= 0.43 on 1 df, p=0.5114
Wald test = 0.53 on 1 df, p=0.4649
Score (logrank) test = 0.56 on 1 df, p=0.4544
Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: READM_HOSP_30_DAYS_F
Radiation Type
uni_var(test_var = "RX_SUMM_RADIATION_F", data_imp = data)
_________________________________________________
## RX_SUMM_RADIATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RX_SUMM_RADIATION_F, data = data)
n events median 0.95LCL 0.95UCL
RX_SUMM_RADIATION_F=None 39 19 22.6 4.76 NA
RX_SUMM_RADIATION_F=Beam Radiation 9 7 12.2 8.08 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RX_SUMM_RADIATION_F, data = data)
RX_SUMM_RADIATION_F=None
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 21 15 0.598 0.0804 0.460 0.779
24 15 4 0.483 0.0831 0.344 0.676
36 11 0 0.483 0.0831 0.344 0.676
48 8 0 0.483 0.0831 0.344 0.676
60 4 0 0.483 0.0831 0.344 0.676
RX_SUMM_RADIATION_F=Beam Radiation
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 6 3 0.667 0.157 0.4200 1.00
24 1 4 0.148 0.133 0.0255 0.86
36 1 0 0.148 0.133 0.0255 0.86
48 1 0 0.148 0.133 0.0255 0.86
## Univariable Cox Proportional Hazard Model for: RX_SUMM_RADIATION_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RX_SUMM_RADIATION_F, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
RX_SUMM_RADIATION_FBeam Radiation 0.3496 1.4185 0.4463 0.783 0.433
exp(coef) exp(-coef) lower .95 upper .95
RX_SUMM_RADIATION_FBeam Radiation 1.419 0.705 0.5915 3.402
Concordance= 0.499 (se = 0.043 )
Rsquare= 0.012 (max possible= 0.976 )
Likelihood ratio test= 0.58 on 1 df, p=0.4459
Wald test = 0.61 on 1 df, p=0.4334
Score (logrank) test = 0.62 on 1 df, p=0.4312
Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: RX_SUMM_RADIATION_F
Lymphovascular Invasion
#uni_var(test_var = "LYMPH_VASCULAR_INVASION_F", data_imp = data)
Endoscopic/Robotic
uni_var(test_var = "RX_HOSP_SURG_APPR_2010_F", data_imp = data)
_________________________________________________
## RX_HOSP_SURG_APPR_2010_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)
n events median 0.95LCL 0.95UCL
RX_HOSP_SURG_APPR_2010_F=No_Surg 42 24 18.7 8.08 NA
RX_HOSP_SURG_APPR_2010_F=Open_Unknown 6 2 NA 7.49 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)
RX_HOSP_SURG_APPR_2010_F=No_Surg
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 24 16 0.610 0.0762 0.477 0.779
24 13 8 0.393 0.0792 0.264 0.583
36 9 0 0.393 0.0792 0.264 0.583
48 7 0 0.393 0.0792 0.264 0.583
60 4 0 0.393 0.0792 0.264 0.583
RX_HOSP_SURG_APPR_2010_F=Open_Unknown
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 3 2 0.6 0.219 0.293 1
24 3 0 0.6 0.219 0.293 1
36 3 0 0.6 0.219 0.293 1
48 2 0 0.6 0.219 0.293 1
## Univariable Cox Proportional Hazard Model for: RX_HOSP_SURG_APPR_2010_F
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
RX_HOSP_SURG_APPR_2010_FOpen_Unknown -0.6234 0.5361 0.7364 -0.847 0.397
exp(coef) exp(-coef) lower .95 upper .95
RX_HOSP_SURG_APPR_2010_FOpen_Unknown 0.5361 1.865 0.1266 2.27
Concordance= 0.531 (se = 0.036 )
Rsquare= 0.018 (max possible= 0.976 )
Likelihood ratio test= 0.86 on 1 df, p=0.3549
Wald test = 0.72 on 1 df, p=0.3973
Score (logrank) test = 0.74 on 1 df, p=0.3896
Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: RX_HOSP_SURG_APPR_2010_F
Surgery Radiation Sequence
uni_var(test_var = "SURG_RAD_SEQ", data_imp = data)
_________________________________________________
## SURG_RAD_SEQ
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SURG_RAD_SEQ, data = data)
n events median 0.95LCL 0.95UCL
SURG_RAD_SEQ=Surg Alone 4 1 NA 4.24 NA
SURG_RAD_SEQ=Surg then Rad 2 1 7.49 7.49 NA
SURG_RAD_SEQ=Rad Alone 7 6 12.19 8.08 NA
SURG_RAD_SEQ=No Treatment 35 18 18.73 4.76 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SURG_RAD_SEQ, data = data)
SURG_RAD_SEQ=Surg Alone
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 2 1 0.667 0.272 0.3 1
24 2 0 0.667 0.272 0.3 1
36 2 0 0.667 0.272 0.3 1
48 1 0 0.667 0.272 0.3 1
SURG_RAD_SEQ=Surg then Rad
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1 1 0.5 0.354 0.125 1
24 1 0 0.5 0.354 0.125 1
36 1 0 0.5 0.354 0.125 1
48 1 0 0.5 0.354 0.125 1
SURG_RAD_SEQ=Rad Alone
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12.000 5.000 2.000 0.714 0.171 0.447 1.000
SURG_RAD_SEQ=No Treatment
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 19 14 0.588 0.0844 0.444 0.779
24 13 4 0.462 0.0868 0.320 0.668
36 9 0 0.462 0.0868 0.320 0.668
48 7 0 0.462 0.0868 0.320 0.668
60 4 0 0.462 0.0868 0.320 0.668
## Univariable Cox Proportional Hazard Model for: SURG_RAD_SEQ
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SURG_RAD_SEQ, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
SURG_RAD_SEQSurg then Rad 0.5227 1.6865 1.4150 0.369 0.712
SURG_RAD_SEQRad Alone 1.1819 3.2605 1.0836 1.091 0.275
SURG_RAD_SEQNo Treatment 0.7629 2.1445 1.0278 0.742 0.458
exp(coef) exp(-coef) lower .95 upper .95
SURG_RAD_SEQSurg then Rad 1.686 0.5929 0.1053 27.01
SURG_RAD_SEQRad Alone 3.261 0.3067 0.3899 27.27
SURG_RAD_SEQNo Treatment 2.145 0.4663 0.2861 16.08
Concordance= 0.534 (se = 0.049 )
Rsquare= 0.035 (max possible= 0.976 )
Likelihood ratio test= 1.71 on 3 df, p=0.6351
Wald test = 1.58 on 3 df, p=0.664
Score (logrank) test = 1.65 on 3 df, p=0.6479
Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: SURG_RAD_SEQ
Surgery Yes/No
uni_var(test_var = "SURGERY_YN", data_imp = data)
_________________________________________________
## SURGERY_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SURGERY_YN, data = data)
n events median 0.95LCL 0.95UCL
SURGERY_YN=No 42 24 18.7 8.08 NA
SURGERY_YN=Yes 6 2 NA 7.49 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SURGERY_YN, data = data)
SURGERY_YN=No
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 24 16 0.610 0.0762 0.477 0.779
24 13 8 0.393 0.0792 0.264 0.583
36 9 0 0.393 0.0792 0.264 0.583
48 7 0 0.393 0.0792 0.264 0.583
60 4 0 0.393 0.0792 0.264 0.583
SURGERY_YN=Yes
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 3 2 0.6 0.219 0.293 1
24 3 0 0.6 0.219 0.293 1
36 3 0 0.6 0.219 0.293 1
48 2 0 0.6 0.219 0.293 1
## Univariable Cox Proportional Hazard Model for: SURGERY_YN
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ SURGERY_YN, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
SURGERY_YNYes -0.6234 0.5361 0.7364 -0.847 0.397
exp(coef) exp(-coef) lower .95 upper .95
SURGERY_YNYes 0.5361 1.865 0.1266 2.27
Concordance= 0.531 (se = 0.036 )
Rsquare= 0.018 (max possible= 0.976 )
Likelihood ratio test= 0.86 on 1 df, p=0.3549
Wald test = 0.72 on 1 df, p=0.3973
Score (logrank) test = 0.74 on 1 df, p=0.3896
## Unadjusted Kaplan Meier Overall Survival Curve for: SURGERY_YN
Radiation Yes/No
uni_var(test_var = "RADIATION_YN", data_imp = data)
_________________________________________________
## RADIATION_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RADIATION_YN, data = data)
n events median 0.95LCL 0.95UCL
RADIATION_YN=No 39 19 22.6 4.76 NA
RADIATION_YN=Yes 9 7 12.2 8.08 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RADIATION_YN, data = data)
RADIATION_YN=No
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 21 15 0.598 0.0804 0.460 0.779
24 15 4 0.483 0.0831 0.344 0.676
36 11 0 0.483 0.0831 0.344 0.676
48 8 0 0.483 0.0831 0.344 0.676
60 4 0 0.483 0.0831 0.344 0.676
RADIATION_YN=Yes
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 6 3 0.667 0.157 0.4200 1.00
24 1 4 0.148 0.133 0.0255 0.86
36 1 0 0.148 0.133 0.0255 0.86
48 1 0 0.148 0.133 0.0255 0.86
## Univariable Cox Proportional Hazard Model for: RADIATION_YN
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ RADIATION_YN, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
RADIATION_YNYes 0.3496 1.4185 0.4463 0.783 0.433
exp(coef) exp(-coef) lower .95 upper .95
RADIATION_YNYes 1.419 0.705 0.5915 3.402
Concordance= 0.499 (se = 0.043 )
Rsquare= 0.012 (max possible= 0.976 )
Likelihood ratio test= 0.58 on 1 df, p=0.4459
Wald test = 0.61 on 1 df, p=0.4334
Score (logrank) test = 0.62 on 1 df, p=0.4312
## Unadjusted Kaplan Meier Overall Survival Curve for: RADIATION_YN
Chemo Yes/No
uni_var(test_var = "CHEMO_YN", data_imp = data)
_________________________________________________
## CHEMO_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ CHEMO_YN, data = data)
n events median 0.95LCL 0.95UCL
CHEMO_YN=No 19 7 NA 18.73 NA
CHEMO_YN=Yes 29 19 12.2 4.76 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ CHEMO_YN, data = data)
CHEMO_YN=No
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 13 4 0.774 0.0997 0.601 0.996
24 8 3 0.577 0.1238 0.379 0.879
36 6 0 0.577 0.1238 0.379 0.879
48 6 0 0.577 0.1238 0.379 0.879
60 2 0 0.577 0.1238 0.379 0.879
CHEMO_YN=Yes
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 14 14 0.517 0.0928 0.364 0.735
24 8 5 0.328 0.0897 0.192 0.561
36 6 0 0.328 0.0897 0.192 0.561
48 3 0 0.328 0.0897 0.192 0.561
60 2 0 0.328 0.0897 0.192 0.561
## Univariable Cox Proportional Hazard Model for: CHEMO_YN
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ CHEMO_YN, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
CHEMO_YNYes 0.6375 1.8918 0.4437 1.437 0.151
exp(coef) exp(-coef) lower .95 upper .95
CHEMO_YNYes 1.892 0.5286 0.7929 4.514
Concordance= 0.562 (se = 0.051 )
Rsquare= 0.046 (max possible= 0.976 )
Likelihood ratio test= 2.25 on 1 df, p=0.1339
Wald test = 2.06 on 1 df, p=0.1507
Score (logrank) test = 2.13 on 1 df, p=0.144
Removed 1 rows containing missing values (geom_errorbar).
## Unadjusted Kaplan Meier Overall Survival Curve for: CHEMO_YN
Treatment Yes/No
uni_var(test_var = "Tx_YN", data_imp = data)
_________________________________________________
## Tx_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ Tx_YN, data = data)
n events median 0.95LCL 0.95UCL
Tx_YN=FALSE 12 5 NA 2.50 NA
Tx_YN=TRUE 36 21 16.4 7.49 NA
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ Tx_YN, data = data)
Tx_YN=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 7 4 0.636 0.145 0.407 0.995
24 6 1 0.545 0.150 0.318 0.936
36 4 0 0.545 0.150 0.318 0.936
48 4 0 0.545 0.150 0.318 0.936
60 2 0 0.545 0.150 0.318 0.936
Tx_YN=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 20 14 0.602 0.0826 0.460 0.788
24 10 7 0.373 0.0860 0.237 0.586
36 8 0 0.373 0.0860 0.237 0.586
48 5 0 0.373 0.0860 0.237 0.586
60 2 0 0.373 0.0860 0.237 0.586
## Univariable Cox Proportional Hazard Model for: Tx_YN
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS ==
0) ~ Tx_YN, data = data)
n= 48, number of events= 26
coef exp(coef) se(coef) z Pr(>|z|)
Tx_YNTRUE 0.2905 1.3371 0.5000 0.581 0.561
exp(coef) exp(-coef) lower .95 upper .95
Tx_YNTRUE 1.337 0.7479 0.5018 3.562
Concordance= 0.503 (se = 0.044 )
Rsquare= 0.007 (max possible= 0.976 )
Likelihood ratio test= 0.36 on 1 df, p=0.55
Wald test = 0.34 on 1 df, p=0.5613
Score (logrank) test = 0.34 on 1 df, p=0.5599
## Unadjusted Kaplan Meier Overall Survival Curve for: Tx_YN
Tumor specific Variables
Node Size
Cox Proportional Hazard Ratio
Model #1
Full analysis
model_one <- coxph(Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)
~ SURG_RAD_SEQ + INSURANCE_F + AGE + SEX_F + RACE_F + INCOME_F + U_R_F +
FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F,
data = data)
model_one %>% summary()
Summary of Model
model_one %>%
tidy(., exponentiate = TRUE) %>%
select(term, estimate, conf.low, conf.high, p.value) %>%
rename(Variable = term,
Hazard_Ratio = estimate) %>%
tbl_df %>%
print(n = nrow(.))
Linear Regression
#only include rows with known treatment information, n = 48. 0 excluded.
data2 <- data %>% filter(SURGERY_YN != "Ukn" & RADIATION_YN != "Ukn"
& CHEMO_YN != "Ukn")
# include only variables with data available for at least 75% of cases (>12)
# from the following set of variables of interest:
## FACILITY_TYPE_F + FACILITY_GEOGRAPHY + CROWFLY +
## DX_STAGING_PROC_DAYS +
## CHEMO_YN + RADIATION_YN + SURGERY_YN + IMMUNO_YN +
## AGE + SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F +
## EDUCATION_F + YEAR_OF_DIAGNOSIS + SITE_TEXT + GRADE_F
length(which(is.na(data2$YEAR_OF_DIAGNOSIS)))
[1] 0
# excluded the following in this analysis due to missing data:
# none
fit_surv <- lm(DX_LASTCONTACT_DEATH_MONTHS ~
FACILITY_GEOGRAPHY + CROWFLY + DX_STAGING_PROC_DAYS +
CHEMO_YN + IMMUNO_YN +
AGE + SEX_F + RACE_F + INSURANCE_F +
YEAR_OF_DIAGNOSIS,
data = data2)
summary(fit_surv) # R^2 = , p =
Call:
lm(formula = DX_LASTCONTACT_DEATH_MONTHS ~ FACILITY_GEOGRAPHY +
CROWFLY + DX_STAGING_PROC_DAYS + CHEMO_YN + IMMUNO_YN + AGE +
SEX_F + RACE_F + INSURANCE_F + YEAR_OF_DIAGNOSIS, data = data2)
Residuals:
Min 1Q Median 3Q Max
-22.562 -3.359 0.000 3.823 19.380
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 166.23394 37.57341 4.424 0.00129 **
FACILITY_GEOGRAPHYSouth 7.27975 10.31614 0.706 0.49651
FACILITY_GEOGRAPHYMidwest 9.10588 11.24548 0.810 0.43694
FACILITY_GEOGRAPHYWest -11.25083 15.25596 -0.737 0.47778
CROWFLY -0.02306 0.05525 -0.417 0.68523
DX_STAGING_PROC_DAYS 0.14873 0.19684 0.756 0.46731
CHEMO_YNYes -31.91791 11.87644 -2.687 0.02280 *
IMMUNO_YNYes 21.35550 16.07901 1.328 0.21364
AGE -1.69099 0.49103 -3.444 0.00629 **
SEX_FFemale 6.69829 8.38290 0.799 0.44283
RACE_FBlack 15.54313 13.04704 1.191 0.26104
RACE_FOther/Unk 19.15127 27.10491 0.707 0.49597
RACE_FAsian 27.99541 19.24807 1.454 0.17648
INSURANCE_FNone -6.07965 15.59046 -0.390 0.70474
INSURANCE_FMedicaid 11.72408 20.84382 0.562 0.58618
INSURANCE_FMedicare 31.38030 15.01753 2.090 0.06317 .
YEAR_OF_DIAGNOSIS2011 -0.35832 22.11062 -0.016 0.98739
YEAR_OF_DIAGNOSIS2012 -43.54602 19.95942 -2.182 0.05409 .
YEAR_OF_DIAGNOSIS2013 -59.57859 18.09339 -3.293 0.00811 **
YEAR_OF_DIAGNOSIS2014 -54.32287 14.47714 -3.752 0.00377 **
YEAR_OF_DIAGNOSIS2015 -44.01667 15.71079 -2.802 0.01874 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 15.91 on 10 degrees of freedom
(17 observations deleted due to missingness)
Multiple R-squared: 0.8785, Adjusted R-squared: 0.6356
F-statistic: 3.616 on 20 and 10 DF, p-value: 0.02054
# the following variables were excluded to
# improve the R-squared of the regression (initially R^2 = ):
# GRADE_F (all were the same), SITE_TEXT, HISPANIC, FACILITY_TYPE_F,
# EDUCATION_F, SURGERY_YN, RADIATION_YN, INCOME_F, EDUCATION_F
Prediction Logistic Regression Models
Surgery
no_Ukns <- data %>%
filter(SURGERY_YN != "Ukn") %>%
droplevels() %>%
mutate(SURGERY_YN = as.logical(SURGERY_YN))
fit_surg <- glm(SURG_TF ~
FACILITY_TYPE_F +
CHEMO_YN + RADIATION_YN +
AGE + SEX_F + RACE_F + INCOME_F +
YEAR_OF_DIAGNOSIS,
data = no_Ukns)
summary(fit_surg)
Call:
glm(formula = SURG_TF ~ FACILITY_TYPE_F + CHEMO_YN + RADIATION_YN +
AGE + SEX_F + RACE_F + INCOME_F + YEAR_OF_DIAGNOSIS, data = no_Ukns)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.52247 -0.13209 -0.02420 0.08866 0.78602
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.228819 0.665972 -1.845 0.0815 .
FACILITY_TYPE_FComprehensive Comm Ca Program 0.541501 0.440128 1.230 0.2344
FACILITY_TYPE_FAcademic/Research Program 0.495231 0.406177 1.219 0.2385
FACILITY_TYPE_FIntegrated Network Ca Program 0.780425 0.453323 1.722 0.1023
CHEMO_YNYes 0.248205 0.154196 1.610 0.1249
RADIATION_YNYes 0.116626 0.151406 0.770 0.4511
AGE 0.003365 0.005590 0.602 0.5548
SEX_FFemale 0.199961 0.144360 1.385 0.1829
RACE_FBlack -0.109713 0.229457 -0.478 0.6383
RACE_FOther/Unk -0.054007 0.290836 -0.186 0.8548
RACE_FAsian -0.211848 0.362495 -0.584 0.5662
INCOME_F$38,000 - $47,999 0.221787 0.201701 1.100 0.2860
INCOME_F$48,000 - $62,999 0.050883 0.198858 0.256 0.8009
INCOME_F$63,000 + 0.413609 0.188578 2.193 0.0417 *
YEAR_OF_DIAGNOSIS2011 -0.077667 0.359923 -0.216 0.8316
YEAR_OF_DIAGNOSIS2012 -0.043183 0.241029 -0.179 0.8598
YEAR_OF_DIAGNOSIS2013 0.467585 0.231423 2.020 0.0585 .
YEAR_OF_DIAGNOSIS2014 0.338811 0.227568 1.489 0.1538
YEAR_OF_DIAGNOSIS2015 0.054832 0.269407 0.204 0.8410
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.09646308)
Null deviance: 3.5676 on 36 degrees of freedom
Residual deviance: 1.7363 on 18 degrees of freedom
(11 observations deleted due to missingness)
AIC: 31.813
Number of Fisher Scoring iterations: 2
# the following variables were excluded to improve the model:
# GRADE_F (all were the same), SITE_TEXT, HISPANIC, FACILITY_TYPE_F,
# EDUCATION_F, RADIATION_YN, INCOME_F, EDUCATION_F
exp(cbind("Odds ratio" = coef(fit_surg), confint.default(fit_surg, level = 0.95)))
Odds ratio 2.5 % 97.5 %
(Intercept) 0.2926379 0.07933299 1.079462
FACILITY_TYPE_FComprehensive Comm Ca Program 1.7185844 0.72532577 4.072008
FACILITY_TYPE_FAcademic/Research Program 1.6408778 0.74018127 3.637595
FACILITY_TYPE_FIntegrated Network Ca Program 2.1823992 0.89756240 5.306446
CHEMO_YNYes 1.2817228 0.94742016 1.733986
RADIATION_YNYes 1.1236992 0.83516723 1.511913
AGE 1.0033702 0.99243722 1.014424
SEX_FFemale 1.2213552 0.92037082 1.620769
RACE_FBlack 0.8960914 0.57152882 1.404968
RACE_FOther/Unk 0.9474251 0.53577787 1.675348
RACE_FAsian 0.8090880 0.39759358 1.646464
INCOME_F$38,000 - $47,999 1.2483050 0.84068280 1.853571
INCOME_F$48,000 - $62,999 1.0521995 0.71257440 1.553696
INCOME_F$63,000 + 1.5122655 1.04498439 2.188499
YEAR_OF_DIAGNOSIS2011 0.9252725 0.45698579 1.873426
YEAR_OF_DIAGNOSIS2012 0.9577363 0.59714724 1.536068
YEAR_OF_DIAGNOSIS2013 1.5961353 1.01410274 2.512219
YEAR_OF_DIAGNOSIS2014 1.4032776 0.89833261 2.192048
YEAR_OF_DIAGNOSIS2015 1.0563636 0.62300826 1.791154
Chemo
Radiation
Immunotherapy
Any Treatment
no_Ukns <- data %>%
filter(Tx_YN != "Ukn") %>%
droplevels()
fit_tx <- glm(Tx_YN ~ FACILITY_TYPE_F + FACILITY_GEOGRAPHY +
AGE + SEX_F + RACE_F + INSURANCE_F + INCOME_F +
EDUCATION_F + YEAR_OF_DIAGNOSIS,
data = no_Ukns)
summary(fit_tx)
Call:
glm(formula = Tx_YN ~ FACILITY_TYPE_F + FACILITY_GEOGRAPHY +
AGE + SEX_F + RACE_F + INSURANCE_F + INCOME_F + EDUCATION_F +
YEAR_OF_DIAGNOSIS, data = no_Ukns)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.52890 -0.09865 0.00000 0.08582 0.65016
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.697114 1.535631 -0.454 0.6596
FACILITY_TYPE_FComprehensive Comm Ca Program 0.032096 1.148611 0.028 0.9783
FACILITY_TYPE_FAcademic/Research Program 0.219129 1.039877 0.211 0.8373
FACILITY_TYPE_FIntegrated Network Ca Program 0.038149 0.910039 0.042 0.9674
FACILITY_GEOGRAPHYSouth 0.098687 0.375467 0.263 0.7980
FACILITY_GEOGRAPHYMidwest -0.304541 0.311524 -0.978 0.3513
FACILITY_GEOGRAPHYWest 0.391416 0.399375 0.980 0.3502
AGE -0.004941 0.011642 -0.424 0.6802
SEX_FFemale -0.112370 0.330255 -0.340 0.7407
RACE_FBlack 0.259911 0.488445 0.532 0.6063
RACE_FOther/Unk 0.925809 0.718425 1.289 0.2265
RACE_FAsian -0.971713 0.783281 -1.241 0.2431
INSURANCE_FNone -0.487590 0.638215 -0.764 0.4625
INSURANCE_FMedicaid -0.540467 0.711507 -0.760 0.4650
INSURANCE_FMedicare 0.194082 0.537306 0.361 0.7255
INSURANCE_FOther Government -0.191456 1.419989 -0.135 0.8954
INCOME_F$38,000 - $47,999 -0.175331 0.549074 -0.319 0.7561
INCOME_F$48,000 - $62,999 -0.752120 0.596470 -1.261 0.2359
INCOME_F$63,000 + -0.701650 0.676859 -1.037 0.3243
EDUCATION_F13 - 20.9% 0.865229 0.500067 1.730 0.1143
EDUCATION_F7 - 12.9% 1.611678 0.701770 2.297 0.0445 *
EDUCATION_FLess than 7% 1.521624 0.679106 2.241 0.0490 *
YEAR_OF_DIAGNOSIS2011 0.939653 0.778316 1.207 0.2551
YEAR_OF_DIAGNOSIS2012 0.855921 0.563313 1.519 0.1596
YEAR_OF_DIAGNOSIS2013 0.989823 0.553169 1.789 0.1038
YEAR_OF_DIAGNOSIS2014 0.743443 0.677944 1.097 0.2985
YEAR_OF_DIAGNOSIS2015 1.290167 0.556770 2.317 0.0430 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 0.2175182)
Null deviance: 7.2973 on 36 degrees of freedom
Residual deviance: 2.1752 on 10 degrees of freedom
(11 observations deleted due to missingness)
AIC: 56.151
Number of Fisher Scoring iterations: 2
exp(cbind("Odds ratio" = coef(fit_rads), confint.default(fit_tx, level = 0.95)))
number of rows of result is not a multiple of vector length (arg 1)
Odds ratio 2.5 % 97.5 %
(Intercept) 1.8297836 0.02455364 10.101330
FACILITY_TYPE_FComprehensive Comm Ca Program 0.8621574 0.10870254 9.809304
FACILITY_TYPE_FAcademic/Research Program 0.4876133 0.16218894 9.556781
FACILITY_TYPE_FIntegrated Network Ca Program 0.7718355 0.17455858 6.182929
FACILITY_GEOGRAPHYSouth 0.7623553 0.52876359 2.303866
FACILITY_GEOGRAPHYMidwest 1.3008497 0.40047030 1.358029
FACILITY_GEOGRAPHYWest 0.7500351 0.67614714 3.235479
AGE 0.7699909 0.97262357 1.018037
SEX_FFemale 0.6492832 0.46782658 1.707308
RACE_FBlack 0.7980793 0.49786621 3.377872
RACE_FOther/Unk 1.0581502 0.61737505 10.318069
RACE_FAsian 0.7098408 0.08151931 1.756793
INSURANCE_FNone 1.2114767 0.17578957 2.145318
INSURANCE_FMedicaid 1.2112700 0.14442502 2.349169
INSURANCE_FMedicare 1.0316648 0.42357735 3.480523
INSURANCE_FOther Government 1.0738527 0.05106869 13.352086
INCOME_F$38,000 - $47,999 0.7020120 0.28607662 2.461656
INCOME_F$48,000 - $62,999 0.6907555 0.14643420 1.517310
INCOME_F$63,000 + 1.0021452 0.13156313 1.868189
EDUCATION_F13 - 20.9% 0.6759286 0.89146852 6.330269
EDUCATION_F7 - 12.9% 0.5257933 1.26646994 19.828527
EDUCATION_FLess than 7% 0.6249400 1.20997711 17.333610
YEAR_OF_DIAGNOSIS2011 0.8512837 0.55665013 11.764948
YEAR_OF_DIAGNOSIS2012 0.5668372 0.78024147 7.099289
YEAR_OF_DIAGNOSIS2013 0.6120539 0.90994834 7.956692
YEAR_OF_DIAGNOSIS2014 0.9904401 0.55693709 7.942184
YEAR_OF_DIAGNOSIS2015 0.9542141 1.22008168 10.820214
Metastasis at Time of Diagnosis
---
title: "Primary Cutaneous Gamma-Delta T-cell Lymphoma"
author: "Ramie Fathy"
date: "12/16/2019"
output:
  html_notebook:
    theme: united
    toc: yes
    toc_float: yes
  html_document:
    toc: yes
---

```{r, echo=FALSE, warning=FALSE, message=FALSE}

library("ggplot2")
library("dplyr")
library("tidyr")
library("knitr")
library("tableone")
library("forcats")
library("survival")
library("npsurv")
library("broom")
library("tibble")
library("readr")
library("survminer")
library("stringr")


knitr::opts_chunk$set(echo=TRUE, warning=FALSE, message=TRUE)
'%!in%' <- function(x,y)!('%in%'(x,y))
```

```{r}
p_table <- function(tab_data, ...) {
  tab_data_2 <- deparse(substitute(tab_data))
  
  table_p <- do.call(CreateTableOne, 
                     list(data = as.name(tab_data_2), includeNA = TRUE, ...))
  table_p_out <- print(table_p,
                       showAllLevels = TRUE,
                       printToggle = FALSE)
  kable(table_p_out,
        align = "c")
}
```

```{r}
uni_var <- function(test_var, data_imp) {

                
        cat("_________________________________________________")
        cat("\n")
        cat("   \n##", test_var)
        cat("\n")
        cat("_________________________________________________")
        cat("\n")

        
        f <- as.formula(paste("Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)",
                              as.name(test_var),
                              sep = " ~ " ))
        
        data_imp_2 <- deparse(substitute(data_imp))

        km_fit <- do.call("survfit", list(formula = f, data = as.name(data_imp_2)))

        print(km_fit)
        cat("\n")

        print(summary(km_fit, times = c(12, 24, 36, 48, 60, 120)))
        cat("\n")


        cat("\n")
        cat("\n")
        cat("   \n## Univariable Cox Proportional Hazard Model for: ", test_var)
        cat("\n")
        cat("\n")


        n_levels <- nlevels(data_imp[[test_var]])

        if(n_levels == 1){
                print("Only one level, no Cox model performed")
                cat("\n")

        } else {


                cox_fit <- do.call("coxph", list(formula = f, data = as.name(data_imp_2)))

                print(summary(cox_fit))
                cat("\n")
                
                do.call("ggforest",
                         list(model = cox_fit, data = as.name(data_imp_2)))


        }

        cat("\n")
        cat("\n")
        cat("\n")
        cat("   \n## Unadjusted Kaplan Meier Overall Survival Curve for: ", test_var)


        p <- do.call("ggsurvplot",
                     list(fit = km_fit, data = as.name(data_imp_2),
                          palette = "jco", censor = FALSE, legend = "right",
                          linetype = "strata", xlab = "Time (Months)"))

        print(p)

}

```

```{r chunk2, cache=TRUE, message=FALSE, warning=FALSE, results='hide'}
col.width <- c(37, 10, 1, 1, 3, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 8, 2, 2, 2, 4, 4, 1, 4, 1, 1,
               1, 3, 2, 2, 8, 2, 5, 5, 5, 4, 5, 5, 5,4, 2, 1, 2, 1, 3, 1, 1, 1, 1, 1, 1, 3,
               3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 8,
               8, 8, 2, 1, 1, 1, 1, 8, 1, 1, 8, 1, 1, 2, 2, 5, 2, 5, 3, 1, 3, 1, 8, 8, 2, 8,
               2, 8, 2, 2, 1, 8, 1, 1, 1, 1, 1, 8, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 3, 1, 1,
               1, 1, 1, 1, 1, 1, 1)

col.names.abr <- c("PUF_CASE_ID", "PUF_FACILITY_ID", "FACILITY_TYPE_CD", "FACILITY_LOCATION_CD",
                   "AGE", "SEX", "RACE", "SPANISH_HISPANIC_ORIGIN", "INSURANCE_STATUS",
                   "MED_INC_QUAR_00", "NO_HSD_QUAR_00", "UR_CD_03", "MED_INC_QUAR_12", "NO_HSD_QUAR_12",
                   "UR_CD_13", "CROWFLY", "CDCC_TOTAL_BEST", "SEQUENCE_NUMBER", "CLASS_OF_CASE",
                   "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "LATERALITY", "HISTOLOGY", "BEHAVIOR", "GRADE",
                   "DIAGNOSTIC_CONFIRMATION", "TUMOR_SIZE", "REGIONAL_NODES_POSITIVE",
                   "REGIONAL_NODES_EXAMINED", "DX_STAGING_PROC_DAYS", "RX_SUMM_DXSTG_PROC", "TNM_CLIN_T",
                   "TNM_CLIN_N", "TNM_CLIN_M", "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                   "TNM_PATH_STAGE_GROUP", "TNM_EDITION_NUMBER", "ANALYTIC_STAGE_GROUP", "CS_METS_AT_DX",
                   "CS_METS_EVAL", "CS_EXTENSION", "CS_TUMOR_SIZEEXT_EVAL", "CS_METS_DX_BONE", "CS_METS_DX_BRAIN",
                   "CS_METS_DX_LIVER", "CS_METS_DX_LUNG", "LYMPH_VASCULAR_INVASION", "CS_SITESPECIFIC_FACTOR_1",
                   "CS_SITESPECIFIC_FACTOR_2", "CS_SITESPECIFIC_FACTOR_3", "CS_SITESPECIFIC_FACTOR_4",
                   "CS_SITESPECIFIC_FACTOR_5", "CS_SITESPECIFIC_FACTOR_6", "CS_SITESPECIFIC_FACTOR_7",
                   "CS_SITESPECIFIC_FACTOR_8", "CS_SITESPECIFIC_FACTOR_9", "CS_SITESPECIFIC_FACTOR_10",
                   "CS_SITESPECIFIC_FACTOR_11", "CS_SITESPECIFIC_FACTOR_12", "CS_SITESPECIFIC_FACTOR_13",
                   "CS_SITESPECIFIC_FACTOR_14", "CS_SITESPECIFIC_FACTOR_15", "CS_SITESPECIFIC_FACTOR_16",
                   "CS_SITESPECIFIC_FACTOR_17", "CS_SITESPECIFIC_FACTOR_18", "CS_SITESPECIFIC_FACTOR_19",
                   "CS_SITESPECIFIC_FACTOR_20", "CS_SITESPECIFIC_FACTOR_21", "CS_SITESPECIFIC_FACTOR_22",
                   "CS_SITESPECIFIC_FACTOR_23", "CS_SITESPECIFIC_FACTOR_24", "CS_SITESPECIFIC_FACTOR_25",
                   "CS_VERSION_LATEST", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS", "DX_DEFSURG_STARTED_DAYS",
                   "RX_SUMM_SURG_PRIM_SITE", "RX_HOSP_SURG_APPR_2010", "RX_SUMM_SURGICAL_MARGINS",
                   "RX_SUMM_SCOPE_REG_LN_SUR", "RX_SUMM_SURG_OTH_REGDIS", "SURG_DISCHARGE_DAYS", "READM_HOSP_30_DAYS",
                   "REASON_FOR_NO_SURGERY", "DX_RAD_STARTED_DAYS", "RX_SUMM_RADIATION", "RAD_LOCATION_OF_RX",
                   "RAD_TREAT_VOL", "RAD_REGIONAL_RX_MODALITY", "RAD_REGIONAL_DOSE_CGY", "RAD_BOOST_RX_MODALITY",
                   "RAD_BOOST_DOSE_CGY", "RAD_NUM_TREAT_VOL", "RX_SUMM_SURGRAD_SEQ", "RAD_ELAPSED_RX_DAYS",
                   "REASON_FOR_NO_RADIATION", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", "RX_SUMM_CHEMO",
                   "DX_HORMONE_STARTED_DAYS", "RX_SUMM_HORMONE", "DX_IMMUNO_STARTED_DAYS", "RX_SUMM_IMMUNOTHERAPY",
                   "RX_SUMM_TRNSPLNT_ENDO", "RX_SUMM_SYSTEMIC_SUR_SEQ", "DX_OTHER_STARTED_DAYS", "RX_SUMM_OTHER",
                   "PALLIATIVE_CARE", "RX_SUMM_TREATMENT_STATUS", "PUF_30_DAY_MORT_CD", "PUF_90_DAY_MORT_CD",
                   "DX_LASTCONTACT_DEATH_MONTHS", "PUF_VITAL_STATUS", "RX_HOSP_SURG_PRIM_SITE", "RX_HOSP_CHEMO",
                   "RX_HOSP_IMMUNOTHERAPY", "RX_HOSP_HORMONE", "RX_HOSP_OTHER", "PUF_MULT_SOURCE", "REFERENCE_DATE_FLAG",
                   "RX_SUMM_SCOPE_REG_LN_2012", "RX_HOSP_DXSTG_PROC", "PALLIATIVE_CARE_HOSP", "TUMOR_SIZE_SUMMARY",
                   "METS_AT_DX_OTHER", "METS_AT_DX_DISTANT_LN", "METS_AT_DX_BONE", "METS_AT_DX_BRAIN",
                   "METS_AT_DX_LIVER", "METS_AT_DX_LUNG", "NO_HSD_QUAR_16", "MED_INC_QUAR_16", "MEDICAID_EXPN_CODE")



#Read in data for each subsite
lip <- read_fwf('NCDBPUF_Lip.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

melanoma <- read_fwf('NCDBPUF_Melanoma.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
                       
skin <- read_fwf('NCDBPUF_OtSkin.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

hodgextr <- read_fwf('NCDBPUF_HodgExtr.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

hodgndal <- read_fwf('NCDBPUF_HodgNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

NHLndal <- read_fwf('NCDBPUF_NHLNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

NHLextr <- read_fwf('NCDBPUF_NHLExtr.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))


breast <-  read_fwf('NCDBPUF_Breast.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

vulva <-  read_fwf('NCDBPUF_Vulva.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

vagina <-  read_fwf('NCDBPUF_Vagina.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

penis <-  read_fwf('NCDBPUF_Penis.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

otleuk <- read_fwf('NCDBPUF_OtLeuk.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
  
otheracuteleuk  <- read_fwf('NCDBPUF_OtAcLeuk.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
  
ALL  <- read_fwf('NCDBPUF_ALymLeuk.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))


#Combine data for all subsites
dat <- bind_rows(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast, 
                 vulva, vagina, penis, NHLextr, otleuk, otheracuteleuk, ALL)

rm(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast, vulva, vagina, 
   penis, NHLextr, otleuk, otheracuteleuk, ALL)

prim_site_text <- data_frame(PRIMARY_SITE = c(
#NHL sites
"C000", 
"C001", 
"C002", 
"C003", 
"C004", 
"C005", 
"C006", 
"C008",
"C009", 
"C019", 
"C020", 
"C021",
"C022", 
"C023", 
"C024", 
"C028", 
"C029",
"C030",
"C031",
"C039", 
"C040", 
"C041", 
"C048",
"C049", 
"C050", 
"C051", 
"C052", 
"C058", 
"C059",
"C060", 
"C061", 
"C062", 
"C068", 
"C069", 
"C079",  
"C098",
"C099",
"C111",
"C142",
"C300",
"C379",
"C420",
"C422",
"C424",

#skin/melanoma
                                 "C440", "C441", "C442", "C443", "C444", "C445",
                                 "C446", "C447", "C448", "C449",
                                 
                                 #breast - nipple
                                 "C500",
                                 
                                 #vagina/vulva
                                 "C510", "C511", "C512", "C518", "C519", "C529",
                                 
                                 #penis
                                 "C600", "C601", "C602", "C608", "C609", "C639",

"C770",
"C771",
"C772",
"C773",
"C774",
"C775",
"C778",
"C779"),

SITE_TEXT = c(
"C00.0 External Lip: Upper NOS",
"C00.1 External Lip: Lower NOS",
"C00.2 External Lip: NOS",
"C00.3 Lip: Upper Mucosa",
"C00.4 Lip: Lower Mucosa",
"C00.5 Lip: Mucosa NOS",
"C00.6 Lip: Commissure",
"C00.8 Lip: Overlapping",
"C00.9 Lip NOS",
"C01.9 Tongue: Base NOS",
"C02.0 Tongue: Dorsal NOS",
"C02.1 Tongue: Border, Tip",
"C02.2 Tongue: Ventral NOS",
"C02.3 Tongue: Anterior NOS",
"C02.4 Lingual Tonsil",
"C02.8 Tongue: Overlapping",
"C02.9 Tongue: NOS",
"C03.0 Gum: Upper",
"C03.1 Gum: Lower",
"C03.9 Gum NOS",
"C04.0 Mouth: Anterior Floor",
"C04.1 Mouth: Lateral Floor",
"C04.8 Mouth: Overlapping Floor",
"C04.9 Floor of Mouth NOS",
"C05.0 Hard Palate",
"C05.1 Soft Palate NOS",
"C05.2 Uvula",
"C05.8 Palate: Overlapping",
"C05.9 Palate NOS",
"C06.0 Cheek Mucosa",
"C06.1 Mouth: Vestibule",
"C06.2 Retromolar Area",
"C06.8 Mouth: Other Overlapping",
"C06.9 Mouth NOS",
"C07.9 Parotid Gland",
  "C09.8 Tonsil: Overlapping",
  "C09.9 Tonsil NOS",
  "C11.1 Nasopharynx: Poster Wall", 
  "C14.2 Waldeyer Ring",
  "C30.0 Nasal Cavity",
  "C37.9 Thymus",
"C42.0 Blood",
  "C42.2 Spleen",
"C42.4 Hematopoietic NOS",

 #skin
"C44.0 Skin of lip, NOS",
"C44.1 Eyelid",
"C44.2 External ear",
"C44.3 Skin of ear and unspecified parts of face",
"C44.4 Skin of scalp and neck",
"C44.5 Skin of trunk",
"C44.6 Skin of upper limb and shoulder",
"C44.7 Skin of lower limb and hip",
"C44.8 Overlapping lesion of skin",
"C44.9 Skin, NOS", 

#breast
"C50.0 Nipple",

#vulva/vagina
"C51.0 Labium majus",
"C51.1 Labium minus",
"C51.2 Clitoris",
"C51.8 Overlapping lesion of vulva",
"C51.9 Vulva, NOS",
"C52.9 Vagina, NOS",

#penis
"C60.0 Prepuce",
"C60.1 Glans penis",
"C60.2 Body of penis",
"C60.8 Overlapping lesion of penis",
"C60.9 Penis",
"C63.2 Scrotum, NOS",

  "C77.0 Lymph Nodes: HeadFaceNeck",
  "C77.1 Intrathoracic Lymph Nodes",
  "C77.2 Intra-abdominal LymphNodes",
  "C77.3 Lymph Nodes of axilla or arm ",
  "C77.4 Lymph Nodes: Leg",
  "C77.5 Pelvic Lymph Nodes",
  "C77.8 Lymph Nodes: multiple region",
  "C77.9 Lymph Node NOS"))


dat <- merge(dat, prim_site_text, by = "PRIMARY_SITE", all.x = TRUE) 

rm(prim_site_text)

# convert numeric variables from character class to numeric class
num_vars <- c("AGE", "CROWFLY", "TUMOR_SIZE", "DX_STAGING_PROC_DAYS", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
              "DX_DEFSURG_STARTED_DAYS", "SURG_DISCHARGE_DAYS", "DX_RAD_STARTED_DAYS",  "RAD_REGIONAL_DOSE_CGY",
              "RAD_BOOST_DOSE_CGY", "RAD_ELAPSED_RX_DAYS", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", 
              "DX_HORMONE_STARTED_DAYS", "DX_OTHER_STARTED_DAYS", "DX_LASTCONTACT_DEATH_MONTHS",
              "RAD_NUM_TREAT_VOL")

dat[num_vars] <- lapply(dat[num_vars], as.numeric)


# convert factor variables from character class to factor class
vars <- names(dat)
fact_vars <- vars[!(vars %in% num_vars)] # basically all of the non-numerics

dat[fact_vars] <- lapply(dat[fact_vars], as.character)
dat[fact_vars] <- lapply(dat[fact_vars], as.factor)

dat <- dat %>%
        mutate(FACILITY_TYPE_F = fct_recode(FACILITY_TYPE_CD,
                                            "Community Cancer Program" = "1",
                                            "Comprehensive Comm Ca Program" = "2",
                                            "Academic/Research Program" = "3",
                                            "Integrated Network Ca Program" = "4",
                                            "Other" = "9")) %>%
        mutate(FACILITY_LOCATION_F = fct_recode(FACILITY_LOCATION_CD,
                                            "New England" = "1",
                                            "Middle Atlantic" = "2",
                                            "South Atlantic" = "3",
                                            "East North Central" = "4",
                                            "East South Central" = "5",
                                            "West North Central" = "6",
                                            "West South Central" = "7",
                                            "Mountain" = "8",
                                            "Pacific" = "9",
                                            "out of US" = "0")) %>%
        mutate(FACILITY_GEOGRAPHY = fct_collapse(FACILITY_LOCATION_CD,
                                                 "Northeast" = c("1", "2"),
                                                 "South" = c("3", "7"),
                                                 "Midwest" = c("4", "5", "6"),
                                                 "West" = c("8", "9"))) %>%
        mutate(AGE_F = cut(AGE, c(0, 54, 64, 74, 100))) %>%
        mutate(AGE_40 = cut(AGE, c(0, 40, 100))) %>%
        mutate(SEX_F = fct_recode(SEX,
                                "Male" = "1",
                                "Female" = "2")) %>%
        mutate(RACE_F = fct_collapse(RACE,
                                "White" = c("01"),
                                "Black" = c("02"),
                                "Asian" = c("04", "05", "06", "07", "08", "10", "11", "12", "13", "14", "15",
                                            "16", "17", "20", "21", "22", "25", "26", "27", "28", "30", "31",
                                            "32", "96", "97"),
                                "Other/Unk" = c("03", "98", "99"))) %>%
        mutate(HISPANIC = fct_collapse(SPANISH_HISPANIC_ORIGIN,
                                       "Yes" = c("1", "2", "3", "4", "5", "6", "7", "8"),
                                       "No" = c("0"),
                                       "Unknown" = c("9"))) %>%
        mutate(INSURANCE_F = fct_recode(INSURANCE_STATUS,
                                         "None" = "0",
                                         "Private" = "1",
                                         "Medicaid" = "2",
                                         "Medicare" = "3",
                                         "Other Government" = "4",
                                         "Unknown" = "9")) %>%
        mutate(INSURANCE_F = fct_relevel(INSURANCE_F,
                                         "Private")) %>%
        mutate(INCOME_F = fct_recode(MED_INC_QUAR_12,
                                      "Less than $38,000" = "1",
                                      "$38,000 - $47,999" = "2",
                                      "$48,000 - $62,999" = "3",
                                      "$63,000 +" = "4")) %>%
        mutate(EDUCATION_F = fct_recode(NO_HSD_QUAR_12,
                                        "21% or more" = "1",
                                        "13 - 20.9%" = "2",
                                        "7 - 12.9%" = "3",
                                        "Less than 7%" = "4")) %>%
        mutate(U_R_F = fct_collapse(UR_CD_13,
                                    "Metro" = c("1", "2", "3"),
                                    "Urban" = c("4", "5", "6", "7"),
                                    "Rural" = c("8", "9"))) %>%
        mutate(CLASS_OF_CASE_F = fct_collapse(CLASS_OF_CASE,
                                              All_Part_Prim = c("10", "11", "12", "13",
                                                                "14", "20", "21", "22"),
                                              Other_Facility = c("00"))) %>%
        mutate(GRADE_F = fct_recode(GRADE,
                                  "Gr I: Well Diff" = "1",
                                  "Gr II: Mod Diff" = "2",
                                  "Gr III: Poor Diff" = "3",
                                  "Gr IV: Undiff/Anaplastic" = "4",
                                  "NA/Unkown" = "9")) %>%
        mutate(HISTOLOGY_F = fct_infreq(HISTOLOGY)) %>%
        mutate(HISTOLOGY_F = factor(HISTOLOGY_F)) %>%
        mutate(HISTOLOGY_F_LIM = fct_lump(HISTOLOGY_F, prop = 0.05)) %>%
        mutate(TNM_CLIN_T = fct_recode(TNM_CLIN_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_T = fct_relevel(TNM_CLIN_T,
                                        "1")) %>%
        mutate(TNM_CLIN_N = fct_recode(TNM_CLIN_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_M = fct_recode(TNM_CLIN_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_recode(TNM_PATH_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_relevel(TNM_PATH_T,
                                        "1")) %>%
        mutate(TNM_PATH_N = fct_recode(TNM_PATH_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_M = fct_recode(TNM_PATH_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_STAGE_GROUP = fct_recode(TNM_CLIN_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_STAGE_GROUP = fct_recode(TNM_PATH_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(MARGINS = fct_recode(RX_SUMM_SURGICAL_MARGINS,
                                    "No Residual" = "0",
                                    "Residual, NOS" = "1",
                                    "Microscopic Resid" = "2",
                                    "Macroscopic Resid" = "3",
                                    "Not evaluable" = "7",
                                    "No surg" = "8",
                                    "Unknown" = "9")) %>%
        mutate(MARGINS_YN = fct_collapse(RX_SUMM_SURGICAL_MARGINS,
                                         "Yes" = c("1", "2", "3"),
                                         "No" = c("0"),
                                         "No surg/Unk/NA" = c("7", "8", "9"))) %>%
        mutate(READM_HOSP_30_DAYS_F = fct_recode(READM_HOSP_30_DAYS,
                                                 "No_Surg_or_No_Readmit" = "0",
                                                 "Unplan_Readmit_Same" = "1",
                                                 "Plan_Readmit_Same" = "2",
                                                 "PlanUnplan_Same" = "3",
                                                 "Unknown" = "4")) %>%
        mutate(RX_SUMM_RADIATION_F = fct_recode(RX_SUMM_RADIATION,
                                                "None" = "0",
                                                "Beam Radiation" = "1",
                                                "Radioactive Implants" = "2",
                                                "Radioisotopes" = "3",
                                                "Beam + Imp or Isotopes" = "4",
                                                "Radiation, NOS" = "5",
                                                "Unknown" = "9")) %>%
        mutate(PUF_30_DAY_MORT_CD_F = fct_recode(PUF_30_DAY_MORT_CD,
                                                 "Alive_30" = "0",
                                                 "Dead_30" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(PUF_90_DAY_MORT_CD_F = fct_recode(PUF_90_DAY_MORT_CD,
                                                 "Alive_90" = "0",
                                                 "Dead_90" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(LYMPH_VASCULAR_INVASION_F = fct_recode(LYMPH_VASCULAR_INVASION,
                                                      "Neg_LymphVasc_Inv" = "0",
                                                      "Pos_LumphVasc_Inv" = "1",
                                                      "N_A" = "8",
                                                      "Unknown" = "9")) %>%
        mutate(RX_HOSP_SURG_APPR_2010_F = fct_recode(RX_HOSP_SURG_APPR_2010,
                                                     "No_Surg" = "0",
                                                     "Robot_Assist" = "1",
                                                     "Robot_to_Open" = "2",
                                                     "Endo_Lap" = "3",
                                                     "Endo_Lap_to_Open" = "4",
                                                     "Open_Unknown" = "5",
                                                     "Unknown" = "9")) %>%
        mutate(All = "All") %>%
        mutate(All = factor(All)) %>%
        mutate(REASON_FOR_NO_SURGERY_F = fct_recode(REASON_FOR_NO_SURGERY,
                                                    "Surg performed" = "0",
                                                    "Surg not recommended" = "1",
                                                    "No surg due to pt factors" = "2",
                                                    "No surg, pt died" = "5",
                                                    "Surg rec, not done" = "6",
                                                    "Surg rec, pt refused" = "7",
                                                    "Surg rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(SURGERY_YN = ifelse(REASON_FOR_NO_SURGERY == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_SURGERY == "9",
                                          "Ukn",
                                          "No"))) %>%
        mutate(SURG_TF = case_when(SURGERY_YN == "Yes" ~ TRUE,
                             SURGERY_YN == "No" ~ FALSE,
                             TRUE ~ NA))  %>%
        mutate(REASON_FOR_NO_RADIATION_F = fct_recode(REASON_FOR_NO_RADIATION,
                                                    "Rad performed" = "0",
                                                    "Rad not recommended" = "1",
                                                    "No Rad due to pt factors" = "2",
                                                    "No Rad, pt died" = "5",
                                                    "Rad rec, not done" = "6",
                                                    "Rad rec, pt refused" = "7",
                                                    "Rad rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(RADIATION_YN = ifelse(REASON_FOR_NO_RADIATION == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_RADIATION == "9",
                                          NA,
                                          "No"))) %>%
        mutate(SURGRAD_SEQ_F = fct_recode(RX_SUMM_SURGRAD_SEQ,
                                                   "None or Surg or Rad" = "0",
                                                   "Rad before Surg" = "2",
                                                   "Surg before Rad" = "3",
                                                   "Rad before and after Surg" = "4",
                                                   "Intraop Rad" = "5",
                                                   "Intraop Rad plus other" = "6",
                                                   "Unknown" = "9")) %>%
        mutate(SURG_RAD_SEQ = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                     "Surg Alone",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                            "Rad Alone",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0",
                                                   "No Treatment",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2",
                                                          "Rad then Surg",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3",
                                                                 "Surg then Rad",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4",
                                                                        "Rad before and after Surg",
                                                                        "Other"))))))) %>%
        mutate(SURG_RAD_SEQ = fct_relevel(SURG_RAD_SEQ,
                                          "Surg Alone",
                                          "Surg then Rad",
                                          "Rad Alone")) %>%
        mutate(CHEMO_YN = fct_collapse(RX_SUMM_CHEMO,
                                       "No" = c("00", "82", "85", "86", "87"),
                                       "Yes" = c("01", "02", "03"),
                                       "Ukn" = c("88", "99"))) %>%
        mutate(IMMUNO_YN = fct_collapse(RX_SUMM_IMMUNOTHERAPY,
                                       "No" = c("00", "82", "85", "86", "87"),
                                       "Yes" = c("01"),
                                       "Ukn" = c("88", "99"))) %>%
        mutate(SURG_RAD_SEQ_C = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                     "Surg, No rad, No Chemo",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                            "Rad, No Surg, No Chemo",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                                   "No Surg, No Rad, No Chemo",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "No",
                                                          "Rad then Surg, No Chemo",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "No",
                                                                 "Surg then Rad, No Chemo",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "No",
                                                                        "Rad before and after Surg, No Chemo",
                                ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                       "Surg, No rad, Yes Chemo",
                                       ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                              "Rad, No Surg, Yes Chemo",
                                              ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                                     "No Surg, No Rad, Yes Chemo",
                                                     ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "Yes",
                                                            "Rad then Surg, Yes Chemo",
                                                            ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "Yes",
                                                                   "Surg then Rad, Yes Chemo",
                                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "Yes",
                                                                          "Rad before and after Surg, Yes Chemo",
                                                                          "Other"))))))))))))) %>%
        mutate(SURG_RAD_SEQ_C = fct_infreq(SURG_RAD_SEQ_C)) %>%
        mutate(T_SIZE = as.numeric(TUMOR_SIZE)) %>%
        mutate(T_SIZE = ifelse(T_SIZE == 0,
                                "No Tumor",
                                ifelse(T_SIZE > 0 & T_SIZE < 10 | T_SIZE == 991,
                                       "< 1 cm",
                                       ifelse(T_SIZE >= 10 & T_SIZE < 20 | T_SIZE == 992,
                                              "1-2 cm",
                                              ifelse(T_SIZE >= 20 & T_SIZE < 30 | T_SIZE == 993,
                                                     "2-3 cm",
                                                     ifelse(T_SIZE >= 30 & T_SIZE < 40 | T_SIZE == 994,
                                                            "3-4 cm",
                                                            ifelse(T_SIZE >= 40 & T_SIZE < 50 | T_SIZE == 995,
                                                                   "4-5 cm",
                                                                   ifelse(T_SIZE >= 50 & T_SIZE < 60 | T_SIZE == 996,
                                                                          "5-6 cm",
                                                                          ifelse(T_SIZE >= 60 & T_SIZE <= 987 |
                                                                                         T_SIZE == 980 | T_SIZE == 989 |
                                                                                         T_SIZE == 997,
                                                                          ">6 cm",
                                                                          ifelse(T_SIZE == 988 | T_SIZE == 999,
                                                                                 "NA_unk",
                                                                                 "Microscopic focus")))))))))) %>%
        mutate(T_SIZE = factor(T_SIZE)) %>%
        mutate(T_SIZE = fct_relevel(T_SIZE,
                                     "No Tumor", "Microscopic focus", "< 1 cm", "1-2 cm", "2-3 cm", "3-4 cm",
                                       "4-5 cm", "5-6 cm", ">6 cm", "NA_unk")) %>%
        mutate(mets_at_dx = case_when(CS_METS_DX_LUNG == "1" ~ "Lung",
                                      CS_METS_DX_BONE == "1" ~ "Bone",
                                      CS_METS_DX_BRAIN == "1" ~ "Brain",
                                      CS_METS_DX_LIVER == "1" ~ "Liver",
                                      TRUE ~ "None/Other/Unk/NA")) %>%
        mutate(MEDICAID_EXPN_CODE = fct_recode(MEDICAID_EXPN_CODE,
                                               "Non-Expansion State" = "0",
                                               "Jan 2014 Expansion States" = "1",
                                               "Early Expansion States (2010-13)" = "2",
                                               "Late Expansion States (> Jan 2014)" = "3",
                                               "Suppressed for Ages 0 - 39" = "9"))  %>%
        mutate(EXPN_GROUP =  case_when(MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Post-Expansion",
                                       
                                       MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% 
                                          c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013") ~ "Pre-Expansion",
               
                                       MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2010", "2011", "2012", "2013", "2014", "2015") ~ "Post-Expansion",
                                       
                                        MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", "2009") ~ "Pre-Expansion",

                                       MEDICAID_EXPN_CODE %in% c("Non-Expansion State") ~ "Pre-Expansion",

                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") ~ "Pre-Expansion",
                    
                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") & 
                                        YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Exclude",
                                       
                                       MEDICAID_EXPN_CODE == "Suppressed for Ages 0 - 39" ~ "Exclude")) %>%
  
  mutate(pre_2014 = YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013")) %>%
  
  mutate(mets_at_dx_F = ifelse(mets_at_dx == "None/Other/Unk/NA", FALSE, TRUE)) %>% 
  
  mutate(Tx_YN = ifelse(SURG_RAD_SEQ == "No Treatment" & CHEMO_YN == "No" & 
                          IMMUNO_YN == "No", FALSE, 
                        ifelse(CHEMO_YN == "Ukn", NA, 
                               TRUE)))

fact_vars_2 <- c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "AGE_F", "SEX_F", "RACE_F",
                 "HISPANIC", "INSURANCE_F", "INCOME_F", "EDUCATION_F", "U_R_F",
                 "CDCC_TOTAL_BEST", "CLASS_OF_CASE_F", "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "HISTOLOGY",
                 "BEHAVIOR", "GRADE_F", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M", "TNM_PATH_STAGE_GROUP",
                 "MARGINS", "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "mets_at_dx")


dat <- dat %>%
        mutate_at(fact_vars_2, funs(factor(.)))

```


# Extract Data of Interest

```{r}


# Non-Hodgkin Lymphoma Sites
site_code <- c( 
#additional sites

 #lip  
  "C000", "C001", "C002", "C003", "C004", "C005","C006", "C008","C009",
                                  
                                 
#skin/melanoma
  "C440", "C441", "C442", "C443", "C444", "C445", "C446", "C447", "C448", "C449",
                                 
 #breast - nipple
  "C500",
                                 
#vagina/vulva
  "C510", "C511", "C512", "C518", "C519", "C529",
                                 
#penis
 "C600", "C601", "C602", "C608", "C609", "C639")

#mycosis fungoides, Sezary syndrome, subcutaneous panniculitis-like T-cell lymphoma,
#cutaneous T-cell lymphoma, NOS, primary cutaneous CD30+T-cell lymphoma, 
#NK/T-cell lymphoma, primary cutaneous gamma-delta T-cell lymphoma, 
#and blastic NK cell lymphoma, respectively

#"9700" = Mycosis Fungoides
  # pagetoid reticulosis

#"9701" = Sezary Syndrome
  # Sezary disease

#"9708" = Subcutaneous panniculitislike T-cell lymphoma

#"9709" = Cutaneous T-cell lymphoma, NOS 
  # Cutaneous lymphoma, NOS (C44._); Primary cutaneous CD4-positive small/medium
  # T-cell lymphoma; Primary cutaneous CD8-positive aggressive
  # epidermotropic cytotoxic T-cell lymphoma

#"9718" = Primary cutaneous CD30+ T-cell lymphoproliferative disorder 
  # Lymphomatoid papulosis (C44._), Primary cutaneous anaplastic large cell,
  # lymphoma (C44._), Primary cutaneous CD30+ large T-cell lymphoma (C44._)


#"9726" = Primary cutaneous gamma-delta T-cell lymphoma

histo_code <- c("9726")

behavior_code <- c("3")

data <- dat %>%
        filter(BEHAVIOR %in% behavior_code) %>%
        filter(PRIMARY_SITE %in% site_code) %>%
        filter(HISTOLOGY %in% histo_code) %>%
        #filter(AGE >= 18) %>%
        filter(is.na(PUF_VITAL_STATUS) == FALSE) %>%
        filter(is.na(DX_LASTCONTACT_DEATH_MONTHS) == FALSE)  %>%
        filter(SEQUENCE_NUMBER == "00") 

#file_path <- c("/Users/beastatlife/Google Drive/Penn/Research/Barbieri/NCDB")
#save(data,
#      file = paste0(file_path, "/CTCL_data.Rda"))
```

```{r loadData}
#load("MF_data.Rda")
```


Data including skin tumors was obtained from the NCBD on October 7, 2019. Cases that were included in this analysis were those with:

1. Site codes: `r site_code`
2. Histology codes: `r histo_code`
3. Behavior codes: `r behavior_code`


Patients were excluded if they didn't have values for either follow up or vital status.

Patients were excluded if they had surgery to a distant site using `RX_SUMM_SURG_OTH_REGDIS`. This was done to avoid confounding of different surgical procedures. We are only interested in surgery at the primary site. These distant site surgeries were being counted in the surgery/radiation sequence and thus to simplify analysis they were removed. 

```{r}

data %>%
        CreateTableOne(data = .,
                     vars = c("RX_SUMM_SURG_OTH_REGDIS"),
                     includeNA = TRUE) %>%
        print(.,
              showAllLevels = TRUE)

data <- data %>%
        filter(RX_SUMM_SURG_OTH_REGDIS == "0") 
```


Race was grouped as white, black, asian, other/unknown
Stage was grouped into 0, I, II, III, IV, NA_Unknown, stage 0 was removed
Whether surgery was performed was based on the `REASON_FOR_NO_SURGERY` variable. The `SURGERY_YN` variable was classified as 'Yes', 'No', or 'Unknown'.


Whether radiation was performed was based on the `REASON_FOR_NO_RADIATION` variable. The `RADIATION_YN` variable was classified as 'Yes', 'No', or 'Unknown'.



#Table of variables for all cases:

```{r}
data <- data %>% droplevels()

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT",  "HISTOLOGY", "BEHAVIOR", "GRADE_F", 
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN", "IMMUNO_YN", "Tx_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE", "EXPN_GROUP", "YEAR_OF_DIAGNOSIS"))
```



```{r}

preExpMedicare  <- nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion" & INSURANCE_F == "Medicare"))
postExpMedicare <- nrow(data %>% filter(EXPN_GROUP == "Post-Expansion" & INSURANCE_F == "Medicare"))

# p = 0.25 when comparing change in proportion of patients with Medicare before and after ACA expansion
prop.test(c(preExpMedicare, postExpMedicare), 
          c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% filter(EXPN_GROUP == "Post-Expansion"))))

preExpNoInsurance <- nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion") %>% 
                            filter(INSURANCE_F == "None"))
postExpNoInsurance <- nrow(data %>% filter(EXPN_GROUP == "Post-Expansion") %>% 
                             filter(INSURANCE_F == "None"))

# Significant decrease in the overall proportion of patients without insurance after ACA expansion 
prop.test(c(preExpNoInsurance, postExpNoInsurance), 
          c(nrow(data %>% filter(EXPN_GROUP == "Pre-Expansion")), nrow(data %>% filter(EXPN_GROUP == "Post-Expansion"))))

p_table(no_Excludes, strata = "EXPN_GROUP", vars = "DX_RX_STARTED_DAYS")

data <- data %>% mutate(Insured = INSURANCE_F != "Unknown")

```




#Kaplan Meier Analysis


##All

```{r}
uni_var(test_var = "All", data_imp = data)
```

##Facility Type
```{r}
uni_var(test_var = "FACILITY_TYPE_F", data_imp = data)
```

##Facility Location

```{r}
uni_var(test_var = "FACILITY_LOCATION_F", data_imp = data)
```

##Facility Geography

```{r}
uni_var(test_var = "FACILITY_GEOGRAPHY", data_imp = data)
```

##Age Group

```{r}
uni_var(test_var = "AGE_F", data_imp = data)
```

##Age Group
```{r}
uni_var(test_var = "AGE_40", data_imp = data)
```

##Gender

```{r}
uni_var(test_var = "SEX_F", data_imp = data)
```

##RACE_F

```{r}
uni_var(test_var = "RACE_F", data_imp = data)
```

##Hispanic

```{r}
uni_var(test_var = "HISPANIC", data_imp = data)
```

##Insurance Status

```{r}
uni_var(test_var = "INSURANCE_F", data_imp = data)
```

##Overall Survival pre/post-ACA expansion

```{r}
uni_var(test_var = "EXPN_GROUP", data_imp = no_Excludes)
```


<!-- ##Income -->

<!-- ```{r} -->
<!-- class(data$INCOME_F) -->
<!-- uni_var(test_var = "INCOME_F", data_imp = data) -->
<!-- ``` -->

##Education

```{r}
uni_var(test_var = "EDUCATION_F", data_imp = data)
```

##Urban/Rural

```{r}
uni_var(test_var = "U_R_F", data_imp = data)
```

##Class (treatment at performing facility)

```{r}
uni_var(test_var = "CLASS_OF_CASE_F", data_imp = data)
```

##Year

```{r}
uni_var(test_var = "YEAR_OF_DIAGNOSIS", data_imp = data)
```

##Primary Site

```{r}
uni_var(test_var = "SITE_TEXT", data_imp = data)
```


##Histology

```{r}
#uni_var(test_var = "HISTOLOGY_F_LIM", data_imp = data)
```

<!-- ##Behavior -->

<!-- ```{r} -->
<!-- uni_var(test_var = "BEHAVIOR", data_imp = data) -->
<!-- ``` -->

##Grade

```{r}
#uni_var(test_var = "GRADE_F", data_imp = data)
```

##Clinical T Stage

```{r}
uni_var(test_var = "TNM_CLIN_T", data_imp = data)
```

##Clinical N Stage

```{r}
uni_var(test_var = "TNM_CLIN_N", data_imp = data)
```

<!-- ##Clinical M Stage -->

<!-- ```{r} -->
<!-- uni_var(test_var = "TNM_CLIN_M", data_imp = data) -->
<!-- ``` -->

##Clinical Stage Group

```{r}
uni_var(test_var = "TNM_CLIN_STAGE_GROUP", data_imp = data)
```

##Pathologic T Stage

```{r}
uni_var(test_var = "TNM_PATH_T", data_imp = data)
```

##Pathologic N Stage

```{r}
uni_var(test_var = "TNM_PATH_N", data_imp = data)
```

##Pathologic M Stage

```{r}
uni_var(test_var = "TNM_PATH_M", data_imp = data)
```

##Pathologic Stage Group

```{r}
uni_var(test_var = "TNM_PATH_STAGE_GROUP", data_imp = data)
```

##Margins
```{r}
#uni_var(test_var = "MARGINS", data_imp = data)
```

##Margins Yes/No
```{r}
#uni_var(test_var = "MARGINS_YN", data_imp = data)
```

##30 Day Readmission

```{r}
uni_var(test_var = "READM_HOSP_30_DAYS_F", data_imp = data)
```

##Radiation Type

```{r}
uni_var(test_var = "RX_SUMM_RADIATION_F", data_imp = data)
```


##Lymphovascular Invasion

```{r}
#uni_var(test_var = "LYMPH_VASCULAR_INVASION_F", data_imp = data)
```

##Endoscopic/Robotic

```{r}
uni_var(test_var = "RX_HOSP_SURG_APPR_2010_F", data_imp = data)
```

##Surgery Radiation Sequence 

```{r}
uni_var(test_var = "SURG_RAD_SEQ", data_imp = data)
```

##Surgery Yes/No

```{r}
uni_var(test_var = "SURGERY_YN", data_imp = data)
```

##Radiation Yes/No

```{r}
uni_var(test_var = "RADIATION_YN", data_imp = data)
```

##Chemo Yes/No

```{r}
uni_var(test_var = "CHEMO_YN", data_imp = data)
```


##Treatment Yes/No
```{r}
uni_var(test_var = "Tx_YN", data_imp = data)
```

##Metastases at Dx
```{r}
uni_var(test_var = "mets_at_dx_F", data_imp = data)
```

<!-- ##Tumor Size -->

<!-- ```{r} -->
<!-- uni_var(test_var = "T_SIZE", data_imp = data) -->
<!-- ``` -->

#Tumor specific Variables


###Node Size


#Cox Proportional Hazard Ratio

##Model #1

###Full analysis

```{r}
model_one <- coxph(Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)
                     ~ SURG_RAD_SEQ + INSURANCE_F + AGE + SEX_F + RACE_F + INCOME_F + U_R_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F,
                     data = data)
model_one %>% summary()


```

###Summary of Model

```{r}
model_one %>%
        tidy(., exponentiate = TRUE) %>%
        select(term, estimate, conf.low, conf.high, p.value) %>%
        rename(Variable = term,
               Hazard_Ratio = estimate) %>%
        tbl_df %>%
        print(n = nrow(.))

```

## Linear Regression 
```{r}

#only include rows with known treatment information, n = 48. 0 excluded.
data2 <- data %>% filter(SURGERY_YN != "Ukn" & RADIATION_YN != "Ukn"
                         & CHEMO_YN != "Ukn")

# include only variables with data available for at least 75% of cases (>12)
# from the following set of variables of interest:

## FACILITY_TYPE_F + FACILITY_GEOGRAPHY + CROWFLY + 
##                 DX_STAGING_PROC_DAYS + 
##                 CHEMO_YN + RADIATION_YN + SURGERY_YN + IMMUNO_YN +
##                 AGE + SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F + 
##                 EDUCATION_F + YEAR_OF_DIAGNOSIS + SITE_TEXT + GRADE_F

length(which(is.na(data2$YEAR_OF_DIAGNOSIS)))

# excluded the following in this analysis due to missing data: 
#  none

fit_surv <- lm(DX_LASTCONTACT_DEATH_MONTHS ~
                 FACILITY_GEOGRAPHY + CROWFLY + DX_STAGING_PROC_DAYS +
                 CHEMO_YN + IMMUNO_YN +
                 AGE + SEX_F + RACE_F + INSURANCE_F + 
                 YEAR_OF_DIAGNOSIS,
   data = data2)

summary(fit_surv) # R^2 = , p = 

# the following variables were excluded to 
# improve the R-squared of the regression (initially R^2 = ):
# GRADE_F (all were the same), SITE_TEXT, HISPANIC, FACILITY_TYPE_F,
# EDUCATION_F, SURGERY_YN, RADIATION_YN, INCOME_F, EDUCATION_F 

```

# Prediction Logistic Regression Models

## Surgery
```{r}

no_Ukns <- data %>%
  filter(SURGERY_YN != "Ukn") %>% 
  droplevels() %>% 
  mutate(SURGERY_YN = as.logical(SURGERY_YN))

fit_surg <- glm(SURG_TF ~ 
                 FACILITY_TYPE_F + 
                 CHEMO_YN + RADIATION_YN + 
                 AGE + SEX_F + RACE_F + INCOME_F + 
                 YEAR_OF_DIAGNOSIS,
   data = no_Ukns)

summary(fit_surg)

# the following variables were excluded to improve the model:
# GRADE_F (all were the same), SITE_TEXT, HISPANIC, FACILITY_TYPE_F,
# EDUCATION_F, RADIATION_YN, INCOME_F, EDUCATION_F 

exp(cbind("Odds ratio" = coef(fit_surg), confint.default(fit_surg, level = 0.95)))
```

## Chemo
```{r}
no_Ukns <- data %>%
  filter(CHEMO_YN != "Ukn") %>% 
  droplevels() %>% 
  mutate(CHEMO_YN = fct_recode(CHEMO_YN,
                                        "0" = "No",
                                        "1" = "Yes")) %>% 
  mutate(CHEMO_YN = as.integer(CHEMO_YN)) %>% 
  mutate(CHEMO_YN = CHEMO_YN - 1)

fit_chemo <- glm(CHEMO_YN ~ AGE_F + PRIMARY_SITE + GRADE_F + SEX_F + RACE_F + INSURANCE_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + EXPN_GROUP + YEAR_OF_DIAGNOSIS,
   data = no_Ukns)

summary(fit_chemo)

exp(cbind("Odds ratio" = coef(fit_chemo), confint.default(fit_chemo, level = 0.95)))
```

## Radiation
```{r}
no_Ukns <- data %>%
  filter(RADIATION_YN != "Ukn") %>%
  droplevels() %>%
  mutate(RADIATION_YN = fct_recode(RADIATION_YN,
                                        "0" = "No",
                                        "1" = "Yes"))%>% 
  mutate(RADIATION_YN = as.integer(RADIATION_YN)) %>% 
  mutate(RADIATION_YN = RADIATION_YN - 1)


fit_rads <- glm(RADIATION_YN ~ AGE_F + PRIMARY_SITE + GRADE_F + SEX_F + RACE_F + INSURANCE_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + EXPN_GROUP + YEAR_OF_DIAGNOSIS,
   data = no_Ukns)


summary(fit_rads)

exp(cbind("Odds ratio" = coef(fit_rads), confint.default(fit_rads, level = 0.95)))
```

## Immunotherapy
```{r}
no_Ukns <- data %>%
  filter(IMMUNO_YN != "Ukn") %>%
  droplevels() %>%
  mutate(IMMUNO_YN = fct_recode(IMMUNO_YN,
                                        "0" = "No",
                                        "1" = "Yes"))%>% 
  mutate(IMMUNO_YN = as.integer(IMMUNO_YN)) %>% 
  mutate(IMMUNO_YN = IMMUNO_YN - 1)


fit_immuno <- glm(IMMUNO_YN ~ AGE_F + PRIMARY_SITE + GRADE_F + SEX_F + RACE_F + INSURANCE_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + EXPN_GROUP + YEAR_OF_DIAGNOSIS,
   data = no_Ukns)


summary(fit_immuno)

exp(cbind("Odds ratio" = coef(fit_immuno), confint.default(fit_immuno, level = 0.95)))
```

## Any Treatment
```{r}
no_Ukns <- data %>%
  filter(Tx_YN != "Ukn") %>%
  droplevels()


fit_tx <- glm(Tx_YN ~ FACILITY_TYPE_F + FACILITY_GEOGRAPHY +
                 AGE + SEX_F + RACE_F + INSURANCE_F + INCOME_F + 
                 EDUCATION_F + YEAR_OF_DIAGNOSIS,
   data = no_Ukns)


summary(fit_tx)

exp(cbind("Odds ratio" = coef(fit_rads), confint.default(fit_tx, level = 0.95)))
```

## Metastasis at Time of Diagnosis
```{r}

fit_mets <- glm(mets_at_dx_F ~ AGE_F + PRIMARY_SITE + GRADE_F + SEX_F + RACE_F + INSURANCE_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F + EXPN_GROUP + YEAR_OF_DIAGNOSIS,
   data = data)

summary(fit_mets)

exp(cbind("Odds ratio" = coef(fit_mets), confint.default(fit_mets, level = 0.95)))
```
